ABSTRACT
BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subtype with poor prognosis. We aimed to determine whether circulating tumor DNA (ctDNA) and circulating tumor cell (CTC) could predict response and long-term outcomes to neoadjuvant chemotherapy (NAC). METHODS: Patients with TNBC were enrolled between 2017-2021 at The University of Texas MD Anderson Cancer Center (Houston, TX). Serial plasma samples were collected at four timepoints: pre-NAC (baseline), 12-weeks after NAC (mid-NAC), after NAC/prior to surgery (post-NAC), and one-year after surgery. ctDNA was quantified using a tumor-informed ctDNA assay (SignateraTM, Natera, Inc.) and CTC enumeration using CellSearch. Wilcoxon and Fisher's exact tests were used for comparisons between groups and Kaplan-Meier analysis used for survival outcomes. RESULTS: In total, 37 patients were enrolled. The mean age was 50 and majority of patients had invasive ductal carcinoma (34, 91.9%) with clinical T2, (25, 67.6%) node-negative disease (21, 56.8%). Baseline ctDNA was detected in 90% (27/30) of patients, of whom 70.4% (19/27) achieved ctDNA clearance by mid-NAC. ctDNA clearance at mid-NAC was significantly associated with pathologic complete response (p = 0.02), whereas CTC clearance was not (p = 0.52). There were no differences in overall survival (OS) and recurrence-free survival (RFS) with positive baseline ctDNA and CTC. However, positive ctDNA at mid-NAC was significantly associated with worse OS and RFS (p = 0.0002 and p = 0.0034, respectively). CONCLUSIONS: Early clearance of ctDNA served as a predictive and prognostic marker in TNBC. Personalized ctDNA monitoring during NAC may help predict response and guide treatment.
Subject(s)
Circulating Tumor DNA , Neoadjuvant Therapy , Neoplastic Cells, Circulating , Triple Negative Breast Neoplasms , Humans , Triple Negative Breast Neoplasms/drug therapy , Triple Negative Breast Neoplasms/blood , Triple Negative Breast Neoplasms/mortality , Triple Negative Breast Neoplasms/genetics , Triple Negative Breast Neoplasms/pathology , Circulating Tumor DNA/blood , Circulating Tumor DNA/genetics , Female , Neoadjuvant Therapy/methods , Middle Aged , Adult , Neoplastic Cells, Circulating/pathology , Neoplastic Cells, Circulating/metabolism , Biomarkers, Tumor/blood , Aged , Prognosis , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Treatment OutcomeABSTRACT
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
Subject(s)
Multiparametric Magnetic Resonance Imaging , Neoadjuvant Therapy , Triple Negative Breast Neoplasms , Humans , Triple Negative Breast Neoplasms/diagnostic imaging , Triple Negative Breast Neoplasms/drug therapy , Triple Negative Breast Neoplasms/therapy , Triple Negative Breast Neoplasms/pathology , Female , Neoadjuvant Therapy/methods , Middle Aged , Multiparametric Magnetic Resonance Imaging/methods , Adult , Aged , Treatment Outcome , ROC Curve , Magnetic Resonance Imaging/methods , RadiomicsABSTRACT
Background: Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Methods: Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. Results: We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. Conclusion: The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.
ABSTRACT
Importance: Biliary tract cancers (BTCs) contain several actionable molecular alterations, including FGFR2, IDH1, ERBB2 (formerly HER2), and KRAS. KRAS allelic variants are found in 20% to 30% of BTCs, and multiple KRAS inhibitors are currently under clinical investigation. Objectives: To describe the genomic landscape, co-sequence variations, immunophenotype, genomic ancestry, and survival outcomes of KRAS-mutated BTCs and to calculate the median overall survival (mOS) for the most common allelic variants. Design, Setting, and Participants: This retrospective, multicenter, pooled cohort study obtained clinical and next-generation sequencing data from multiple databases between January 1, 2017, and December 31, 2022. These databases included Princess Margaret Cancer Centre, MD Anderson Cancer Center, Foundation Medicine, American Association for Cancer Research Project GENIE, and cBioPortal for Cancer Genomics. The cohort comprised patients with BTCs who underwent genomic testing. Main Outcome and Measure: The main outcome was mOS, defined as date of diagnosis to date of death, which was measured in months. Results: A total of 7457 patients (n = 3773 males [50.6%]; mean [SD] age, 63 [5] years) with BTCs and genomic testing were included. Of these patients, 5813 had clinical outcome data available, in whom 1000 KRAS-mutated BTCs were identified. KRAS allelic variants were highly prevalent in perihilar cholangiocarcinoma (28.6%) and extrahepatic cholangiocarcinoma (36.1%). Thirty-six KRAS allelic variants were identified, and the prevalence rates in descending order were G12D (41%), G12V (23%), and Q61H (8%). The variant G12D had the highest mOS of 25.1 (95% CI, 22.0-33.0) months compared with 22.8 (95% CI, 19.6-31.4) months for Q61H and 17.8 (95% CI, 16.3-23.1) months for G12V variants. The majority of KRAS-mutated BTCs (98.9%) were not microsatellite instability-high and had low tumor mutational burden (ranging from a median [IQR] of 1.2 (1.2-2.5) to a mean [SD] of 3.3 [1.3]). Immune profiling through RNA sequencing of KRAS and NRAS-mutated samples showed a pattern toward a more immune-inflamed microenvironment with higher M1 macrophage activation (0.16 vs 0.12; P = .047) and interferon-γ expression compared with wild-type tumors. The G12D variant remained the most common KRAS allelic variant in all patient ancestries. Patients with admixed American ancestry had the highest proportion of G12D variant (45.0%). Conclusions and Relevance: This cohort study found that KRAS allelic variants were relatively common and may be potentially actionable genomic alterations in patients with BTCs, especially perihilar cholangiocarcinoma and extrahepatic cholangiocarcinoma. The findings add to the growing data on genomic and immune landscapes of KRAS allelic variants in BTCs and are potentially of value to the planning of specific therapies for this heterogeneous patient group.
Subject(s)
Alleles , Biliary Tract Neoplasms , Proto-Oncogene Proteins p21(ras) , Humans , Male , Female , Middle Aged , Proto-Oncogene Proteins p21(ras)/genetics , Biliary Tract Neoplasms/genetics , Biliary Tract Neoplasms/mortality , Retrospective Studies , Aged , Mutation , Cholangiocarcinoma/genetics , Cholangiocarcinoma/mortalityABSTRACT
PURPOSE: Isocitrate dehydrogenase (IDH)1/2 genomic alterations (GA) occur in 20% of intrahepatic cholangiocarcinoma (iCCA); however, the immunogenomic landscape of IDH1-/2-mutated iCCA is largely unknown. METHODS: Comprehensive genomic profiling (CGP) was performed on 3,067 cases of advanced iCCA. Tumor mutational burden (TMB), PD-L1 expression (Dako 22C3), microsatellite instability (MSI), and genomic loss of heterozygosity (gLOH) as a surrogate marker for homologous recombination deficiency were examined. RNA sequencing of 73 patient samples was analyzed for differences in stromal/immune cell infiltration, immune marker expression, and T-cell inflammation. Tissue microarray arrays were subjected to multiplex immunohistochemistry and colocalization analysis in 100 surgical samples. Retrospective clinical data were collected for 501 patients with cholangiocarcinoma to examine median overall survival (mOS) in IDH1/2+ versus IDHwt. RESULTS: Of 3,067 iCCA cases subjected to CGP, 426 (14%) were IDH1+ and 125 (4%) were IDH2+. IDH1 GA included R132C (69%) and R132L/G/S/H/F (16%/7%/4%/3%/<1%). IDH2 GA occurred at R172 (94.4%) and R140 (6.6%). No significant difference was seen in median gLOH between IDH1+ versus IDHwt iCCA (P = .37), although patterns of comutations differed. MSI-High (P = .009), TMB ≥10 mut/Mb (P < .0001), and PD-L1 positivity were lower in IDH1/2+ versus IDHwt iCCA. Resting natural killer cell population, CD70, and programmed cell death 1 expression were significantly higher in non-IDH1-mutated cases, whereas V-set domain containing T-cell activation inhibitor 1 (B7-H4) expression was significantly higher in IDH1+. No significant difference in mOS was observed between IDH1/2+ versus IDHwt patients. CONCLUSION: Significant differences in GA and immune biomarkers are noted between IDH1/2+ and IDHwt iCCA. IDH1-/2-mutated tumors appear immunologically cold without gLOH. These immunogenomic data provide insight for precision targeting of iCCA with IDH alterations.
Subject(s)
Bile Duct Neoplasms , Cholangiocarcinoma , Isocitrate Dehydrogenase , Humans , B7-H1 Antigen/genetics , Bile Duct Neoplasms/genetics , Bile Duct Neoplasms/pathology , Bile Ducts, Intrahepatic/pathology , Biomarkers, Tumor/genetics , Biomarkers, Tumor/analysis , Cholangiocarcinoma/genetics , Cholangiocarcinoma/pathology , Isocitrate Dehydrogenase/genetics , Mutation , Retrospective StudiesABSTRACT
Zanidatamab is a bispecific human epidermal growth factor receptor 2 (HER2)-targeted antibody that has demonstrated antitumor activity in a broad range of HER2-amplified/expressing solid tumors. We determined the antitumor activity of zanidatamab in patient-derived xenograft (PDX) models developed from pretreatment or postprogression biopsies on the first-in-human zanidatamab phase I study (NCT02892123). Of 36 tumors implanted, 19 PDX models were established (52.7% take rate) from 17 patients. Established PDXs represented a broad range of HER2-expressing cancers, and in vivo testing demonstrated an association between antitumor activity in PDXs and matched patients in 7 of 8 co-clinical models tested. We also identified amplification of MET as a potential mechanism of acquired resistance to zanidatamab and demonstrated that MET inhibitors have single-agent activity and can enhance zanidatamab activity in vitro and in vivo. These findings provide evidence that PDXs can be developed from pretreatment biopsies in clinical trials and may provide insight into mechanisms of resistance. SIGNIFICANCE: We demonstrate that PDXs can be developed from pretreatment and postprogression biopsies in clinical trials and may represent a powerful preclinical tool. We identified amplification of MET as a potential mechanism of acquired resistance to the HER2 inhibitor zanidatamab and MET inhibitors alone and in combination as a therapeutic strategy. This article is featured in Selected Articles from This Issue, p. 695.
Subject(s)
Antibodies, Bispecific , Receptor, ErbB-2 , Animals , Humans , Mice , Antibodies, Bispecific/pharmacology , Antibodies, Bispecific/therapeutic use , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal, Humanized/pharmacology , Receptor, ErbB-2/antagonists & inhibitors , Xenograft Model Antitumor AssaysABSTRACT
The TP53 tumor suppressor gene is mutated early in most of the patients with triple-negative breast cancer (TNBC). The most frequent TP53 alterations are missense mutations that contribute to tumor aggressiveness. Here, we used an autochthonous somatic TNBC mouse model, in which mutant p53 can be toggled on and off genetically while leaving the tumor microenvironment intact and wild-type for p53 to identify physiological dependencies on mutant p53. In TNBCs that develop in this model, deletion of two different hotspot p53R172H and p53R245W mutants triggers ferroptosis in vivo, a cell death mechanism involving iron-dependent lipid peroxidation. Mutant p53 protects cells from ferroptosis inducers, and ferroptosis inhibitors reverse the effects of mutant p53 loss in vivo. Single-cell transcriptomic data revealed that mutant p53 protects cells from undergoing ferroptosis through NRF2-dependent regulation of Mgst3 and Prdx6, which encode two glutathione-dependent peroxidases that detoxify lipid peroxides. Thus, mutant p53 protects TNBCs from ferroptotic death.
Subject(s)
Adenocarcinoma , Ferroptosis , Triple Negative Breast Neoplasms , Animals , Humans , Mice , Cell Line, Tumor , Ferroptosis/genetics , Triple Negative Breast Neoplasms/pathology , Tumor Microenvironment , Tumor Suppressor Protein p53/genetics , Tumor Suppressor Protein p53/metabolismABSTRACT
BACKGROUND: Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). PURPOSE: To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. STUDY TYPE: Prospective. POPULATION: Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). FIELD STRENGTH/SEQUENCE: 3.0 T/reduced field of view single-shot echo-planar DTI sequence. ASSESSMENT: Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. STATISTICAL TESTS: Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. RESULTS: 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). DATA CONCLUSION: Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.
ABSTRACT
Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
ABSTRACT
Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.
ABSTRACT
Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
ABSTRACT
Determining concise sets of genomic markers that identify cell types and states within tissue ecosystems remains challenging. To address this challenge, we developed Recurrent Composite Markers for Biological Identities with Neighborhood Enrichment (RECOMBINE). Validations of RECOMBINE with simulation and transcriptomics data in bulk, single-cell and spatial resolutions demonstrated the method's ability for unbiased selection of composite markers that characterize biological subpopulations. RECOMBINE captured markers of mouse visual cortex from single-cell RNA sequencing data and provided a gene panel for targeted spatial transcriptomics profiling. RECOMBINE identified composite markers of CD8 T cell states including GZMK + HAVCR2 - effector memory cells associated with anti-PD1 therapy response. The method outperformed differential gene expression analysis in characterizing a rare cell subpopulation within mouse intestine. Using RECOMBINE, we uncovered hierarchical gene programs of inter- and intra-tumoral heterogeneity in breast and skin tumors. In conclusion, RECOMBINE offers a data-driven approach for unbiased selection of composite markers, resulting in improved interpretation, discovery, and validation of cell types and states.
ABSTRACT
Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.
ABSTRACT
The interactions between tumor intrinsic processes and immune checkpoints can mediate immune evasion by cancer cells and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies and statistically validates tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The outcome is a catalog of tumor-immune checkpoint interaction maps for diverse immune-associated phenotypes. Applications of ImogiMap recapitulate the interaction of SERPINB9 and immune checkpoints with interferon gamma (IFNγ) expression. Our analyses suggest that CD86-CD70 and CD274-CD70 immunoregulatory interactions are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer, respectively. The open-source ImogiMap software and user-friendly web application will enable future applications of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and immunotherapy targets.
Subject(s)
Neoplasms , Humans , Neoplasms/genetics , Neoplasms/therapy , Immunotherapy , Interferon-gamma/geneticsABSTRACT
Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.
ABSTRACT
Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer.
Subject(s)
Breast Neoplasms , Breast Neoplasms/drug therapy , Breast Neoplasms/genetics , Cell Line, Tumor , ErbB Receptors/metabolism , Fatty Acids , Female , Humans , Lipids , Myeloid Cell Leukemia Sequence 1 Protein/metabolism , Up-RegulationABSTRACT
Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic coalterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multiomic data, the method maps recurrent coalteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent coalteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and preclinical studies. SIGNIFICANCE: We developed the predictive bioinformatics platform REFLECT and a multiomics- based precision combination therapy resource. The REFLECT-selected therapies lead to significant improvements in efficacy and patient survival in preclinical and clinical settings. Use of REFLECT can optimize therapeutic benefit through selection of drug combinations tailored to molecular signatures of tumors. See related commentary by Pugh and Haibe-Kains, p. 1416. This article is highlighted in the In This Issue feature, p. 1397.
Subject(s)
Neoplasms , Oncogenes , Carcinogenesis , Computational Biology/methods , Humans , Immunotherapy , Neoplasms/drug therapy , Neoplasms/genetics , Neoplasms/pathologyABSTRACT
BACKGROUND AND AIMS: Cholangiocarcinoma (CCA) is a deadly and highly therapy-refractory cancer of the bile ducts, with early results from immune checkpoint blockade trials showing limited responses. Whereas recent molecular assessments have made bulk characterizations of immune profiles and their genomic correlates, spatial assessments may reveal actionable insights. APPROACH AND RESULTS: Here, we have integrated immune checkpoint-directed immunohistochemistry with next-generation sequencing of resected intrahepatic CCA samples from 96 patients. We found that both T-cell and immune checkpoint markers are enriched at the tumor margins compared to the tumor center. Using two approaches, we identify high programmed cell death protein 1 or lymphocyte-activation gene 3 and low CD3/CD4/inducible T-cell costimulator specifically in the tumor center as associated with poor survival. Moreover, loss-of-function BRCA1-associated protein-1 mutations are associated with and cause elevated expression of the immunosuppressive checkpoint marker, B7 homolog 4. CONCLUSIONS: This study provides a foundation on which to rationally improve and tailor immunotherapy approaches for this difficult-to-treat disease.
Subject(s)
Antigens, CD/metabolism , Bile Duct Neoplasms/genetics , Bile Duct Neoplasms/metabolism , Cholangiocarcinoma/genetics , Cholangiocarcinoma/metabolism , Programmed Cell Death 1 Receptor/metabolism , Adult , Aged , Aged, 80 and over , Antigens, CD/genetics , B7 Antigens/genetics , Bile Duct Neoplasms/immunology , Bile Ducts, Intrahepatic , Biomarkers, Tumor/genetics , Biomarkers, Tumor/metabolism , CD4-Positive T-Lymphocytes , Cell Line, Tumor , Cholangiocarcinoma/immunology , Female , Gene Expression , Genes, Tumor Suppressor , Genomics , High-Throughput Nucleotide Sequencing , Humans , Immunohistochemistry , Inducible T-Cell Co-Stimulator Protein/genetics , Inducible T-Cell Co-Stimulator Protein/metabolism , Loss of Function Mutation , Male , Middle Aged , Oncogenes/genetics , Programmed Cell Death 1 Receptor/genetics , Survival Rate , Tumor Suppressor Proteins/genetics , Ubiquitin Thiolesterase/genetics , V-Set Domain-Containing T-Cell Activation Inhibitor 1/genetics , Young Adult , Lymphocyte Activation Gene 3 ProteinABSTRACT
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
Subject(s)
Protein Interaction Mapping/methods , Proteins , Proteomics/methods , Signal Transduction/physiology , Causality , Databases, Protein , Humans , Proteins/metabolism , Proteins/physiology , SoftwareABSTRACT
We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.