THE PHARMACOGENOMICS OF EGFR-DEPENDENT NSCLC: PREDICTING AND ENHANCING RESPONSE TO TARGETED EGFR THERAPY

The introduction of tyrosine kinase inhibitors (TKI) targeting the epidermal growth factor receptor (EGFR) inhibitors to the clinic has resulted in an improvement in the treatment of non small cell lung cancer (NSCLC). However, many patients treated with EGFR TKIs do not respond to therapy. The burden of failed treatment is largely placed on the healthcare field, limiting the effectiveness of EGFR TKIs. Furthermore, responses are hindered by the emergence of resistance. Thus, two questions must be addressed to achieve maximum benefit of EGFR inhibitors: How can patients who will benefit from EGFR TKIs be selected a priori? How can patients who respond achieve maximal benefit? To answer these questions, two hypotheses were formed. First, the EGFR-dependent phenotype, which is displayed by the tumors cells of those patients who respond clinically to EGFR TKIs, can be captured by genomic profiling of NSCLC cell lines stratified by sensitivity to EGFR TKIs. This gene signature may be used to predict the outcome of EGFR TKI therapy in unknown samples. Secondly, the predictive signature of response to EGFR TKI could provide insights into the underlying biology of the phenotype of EGFR-dependency. This information could be exploited to identify inhibitors which could be combined with EGFR inhibitors to elicit a greater effect, thereby minimizing resistance. The work herein describes the testing of these hypotheses. Pharmacogenomics was utilized to define a signature of EGFR-dependency which effectively predicted response to EGFR TKI in vitro and in vivo. Furthermore, the signature was analyzed by bioinformatic approaches to identify the RAS/MAPK pathway as a candidate target in EGFR-dependent NSCLC. The RAS/MAPK pathway regulates expression and activation of EGF-like ligands. Furthermore, the RAS/MAPK pathway modulates EGFR stability in the EGFR-dependent phenotype. Further biochemical analyses demonstrated that the RAS/MAPK pathway mediates proliferation and survival of EGFR-dependent NSCLC cells. Finally, combinatorial treatment of EGFR-dependent NSCLC cell lines with small molecules targeting EGFR and the RAS/MAPK pathway yielded cytotoxic synergy. Thus, we have used pharmacogenomics methods to potentially improve NSCLC treatment by developing a method of predicting response and identifying an additional target to combine with EGFR TKIs to maximize responses

Therapeutic potential of DNA methyltransferase inhibitors with immune checkpoint inhibitor therapy in breast cancer

Immune checkpoint inhibitor (ICI) therapy has changed the landscape of cancer treatment, particularly for high-mutation burden cancers. However, ICI therapy has thus far demonstrated limited efficacy in breast cancers, where tumor mutation rates are intermediate. Nonetheless, because of limited but positive signals in early trials, combinations of therapies to enhance anti-tumor immunity, and thus response to ICIs in breast cancer, are actively being sought. Our laboratory recently found that guadecitabine, a next-generation DNA methyltransferase inhibitor (DMTi), potentiated cytotoxic CD8+ T cell responses in breast cancer, which appeared to occur by the following mechanisms: (1) DMTi treatment hypomethylated and up-regulated both baseline and IFN-γ-induced MHC-I expression, thereby enhancing antigen presentation capacity, (2) DMTi treatment increased Cxcr3 ligands/chemokines (i.e., Cxcl9, Cxcl10, and Cxcl11) expression and recruited cytotoxic CD8+ T cells into the tumors and (3) DMTi treatment activated NFκB signaling, presumably through the expression of endogenous retroviral (ERV) sequences in tumor cells, initiating an innate response observed in other solid tumor types [Luo et al., Nat Commun 9(1):248]. Most importantly, DMTi treatment primed breast cancer and improved responses to anti-PD-L1 therapy

Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors

BACKGROUND: Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, and epithelial to mesenchymal transition markers all correlate with EGFR TKI sensitivity, and while prediction of sensitivity using any one of the markers does identify responders, individual markers do not encompass all potential responders due to high levels of inter-patient and inter-tumor variability. We hypothesized that a multivariate predictor of EGFR TKI sensitivity based on gene expression data would offer a clinically useful method of accounting for the increased variability inherent in predicting response to EGFR TKI and for elucidation of mechanisms of aberrant EGFR signalling. Furthermore, we anticipated that this methodology would result in improved predictions compared to single parameters alone both in vitro and in vivo. RESULTS: Gene expression data derived from cell lines that demonstrate differential sensitivity to EGFR TKI, such as erlotinib, were used to generate models for a priori prediction of response. The gene expression signature of EGFR TKI sensitivity displays significant biological relevance in lung cancer biology in that pertinent signalling molecules and downstream effector molecules are present in the signature. Diagonal linear discriminant analysis using this gene signature was highly effective in classifying out-of-sample cancer cell lines by sensitivity to EGFR inhibition, and was more accurate than classifying by mutational status alone. Using the same predictor, we classified human lung adenocarcinomas and captured the majority of tumors with high levels of EGFR activation as well as those harbouring activating mutations in the kinase domain. We have demonstrated that predictive models of EGFR TKI sensitivity can classify both out-of-sample cell lines and lung adenocarcinomas. CONCLUSION: These data suggest that multivariate predictors of response to EGFR TKI have potential for clinical use and likely provide a robust and accurate predictor of EGFR TKI sensitivity that is not achieved with single biomarkers or clinical characteristics in non-small cell lung cancers

Artificial Image Objects for Classification of Breast Cancer Biomarkers With Transcriptome Sequencing Data and Convolutional Neural Network Algorithms

Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results: With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions: We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients

MUC1 Is a Downstream Target of STAT3 and Regulates Lung Cancer Cell Survival and Invasion

Signal transducer and activator of transcription 3 (STAT3) is aberrantly activated in human cancer including lung cancer and has been implicated in transformation, tumorigenicity, and metastasis. One putative downstream gene regulated by Stat3 is MUC1 which also has important roles in tumorigenesis. We determined if Stat3 regulates MUC1 in lung cancer cell lines and what function MUC1 plays in lung cancer cell biology. We examined MUC1 expression in non-small cell lung cancer (NSCLC) cell lines and found high levels of MUC1 protein expression associated with higher levels of tyrosine phosphorylated STAT3. STAT3 knockdown downregulated MUC1 expression whereas constitutive STAT3 expression increased MUC1 expression at mRNA and protein levels. MUC1 knockdown induced cellular apoptosis concomitant with reduced Bcl-XL and sensitized cells to cisplatin treatment. MUC1 knockdown inhibited tumor growth and metastasis in an orthotopic mouse model of lung cancer by activating apoptosis and inhibiting cell proliferation in vivo. These results demonstrate that constitutively activated STAT3 regulates expression of MUC1, which mediates lung cancer cell survival and metastasis in vitro and in vivo. MUC1 appears to be a cooperating oncoprotein with multiple oncogenic tyrosine kinase pathways and could be an effective target for the treatment of lung cancer

A gene expression predictor of response to EGFR-targeted therapy stratifies progression-free survival to cetuximab in KRAS wild-type metastatic colorectal cancer

<p>Abstract</p> <p>Background</p> <p>The anti-EGFR monoclonal antibody cetuximab is used in metastatic colorectal cancer (CRC), and predicting responsive patients garners great interest, due to the high cost of therapy. Mutations in the KRAS gene occur in ~40% of CRC and are a negative predictor of response to cetuximab. However, many KRAS-wildtype patients do not benefit from cetuximab. We previously published a gene expression predictor of sensitivity to erlotinib, an EGFR inhibitor. The purpose of this study was to determine if this predictor could identify KRAS-wildtype CRC patients who will benefit from cetuximab therapy.</p> <p>Methods</p> <p>Microarray data from 80 metastatic CRC patients subsequently treated with cetuximab were extracted from the study by Khambata-Ford et al. The study included KRAS status, response, and PFS for each patient. The gene expression data were scaled and analyzed using our predictive model. An improved predictive model of response was identified by removing features in the 180-gene predictor that introduced noise.</p> <p>Results</p> <p>Forty-three of eighty patients were identified as harboring wildtype-KRAS. When the model was applied to these patients, the predicted-sensitive group had significantly longer PFS than the predicted-resistant group (median 88 days vs. 56 days; mean 117 days vs. 63 days, respectively, p = 0.008). Kaplan-Meier curves were also significantly improved in the predicted-sensitive group (p = 0.0059, HR = 0.4109. The model was simplified to 26 of the original 180 genes and this further improved stratification of PFS (median 147 days vs. 56.5 days in the predicted sensitive and resistant groups, respectively, p < 0.0001). However, the simplified model will require further external validation, as features were selected based on their correlation to PFS in this dataset.</p> <p>Conclusion</p> <p>Our model of sensitivity to EGFR inhibition stratified PFS following cetuximab in KRAS-wildtype CRC patients. This study represents the first true external validation of a molecular predictor of response to cetuximab in KRAS-WT metastatic CRC. Our model may hold clinical utility for identifying patients responsive to cetuximab and may therefore minimize toxicity and cost while maximizing benefit.</p

Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs subtypes include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Through a comprehensive analysis of mutation, copy number, transcriptomic, epigenetic, proteomic, and phospho-proteomic patterns we describe the genomic landscape of TNBC subtypes. Mesenchymal subtype tumors display high mutation loads, genomic instability, absence of immune cells, low PD-L1 expression, decreased global DNA methylation, and transcriptional repression of antigen presentation genes. We demonstrate that major histocompatibility complex I (MHC-I) is transcriptionally suppressed by H3K27me3 modifications by the polycomb repressor complex 2 (PRC2). Pharmacological inhibition of PRC2 subunits EZH2 or EED restores MHC-I expression and enhances chemotherapy efficacy in murine tumor models, providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Subtype-specific differences in immune cell composition and differential genetic/pharmacological vulnerabilities suggest additional treatment strategies for TNBC

If we build it they will come: targeting the immune response to breast cancer.

Historically, breast cancer tumors have been considered immunologically quiescent, with the majority of tumors demonstrating low lymphocyte infiltration, low mutational burden, and modest objective response rates to anti-PD-1/PD-L1 monotherapy. Tumor and immunologic profiling has shed light on potential mechanisms of immune evasion in breast cancer, as well as unique aspects of the tumor microenvironment (TME). These include elements associated with antigen processing and presentation as well as immunosuppressive elements, which may be targeted therapeutically. Examples of such therapeutic strategies include efforts to (1) expand effector T-cells, natural killer (NK) cells and immunostimulatory dendritic cells (DCs), (2) improve antigen presentation, and (3) decrease inhibitory cytokines, tumor-associated M2 macrophages, regulatory T- and B-cells and myeloid derived suppressor cells (MDSCs). The goal of these approaches is to alter the TME, thereby making breast tumors more responsive to immunotherapy. In this review, we summarize key developments in our understanding of antitumor immunity in breast cancer, as well as emerging therapeutic modalities that may leverage that understanding to overcome immunologic resistance

Conditional Loss of ErbB3 Delays Mammary Gland Hyperplasia Induced by Mutant PIK3CA without Affecting Mammary Tumor Latency, Gene Expression, or Signaling

Mutations in PIK3CA, the gene encoding the p110α catalytic subunit of phosphatidylinositol-3 kinase (PI3K), have been shown to transform mammary epithelial cells (MECs). Studies suggest this transforming activity requires binding of mutant p110α via p85 to phosphorylated YXXM motifs in activated receptor tyrosine kinases (RTKs) or adaptors. Using transgenic mice, we examined if ErbB3, a potent activator of PI3K, is required for mutant PIK3CA-mediated transformation of MECs. Conditional loss of ErbB3 in mammary epithelium resulted in a delay of PIK3CAH1047R-dependent mammary gland hyperplasia, but tumor latency, gene expression and PI3K signaling were unaffected. In ErbB3-deficient tumors, mutant PI3K remained associated with several tyrosyl phosphoproteins, potentially explaining the dispensability of ErbB3 for tumorigenicity and PI3K activity. Similarly, inhibition of ErbB RTKs with lapatinib did not affect PI3K signaling in PIK3CAH1047R-expressing tumors. However, the p110α-specific inhibitor BYL719, in combination with lapatinib impaired mammary tumor growth and PI3K signaling more potently than BYL719 alone. Further, co-inhibition of p110α and ErbB3 potently suppressed proliferation and PI3K signaling in human breast cancer cells harboring PIK3CAH1047R. These data suggest that PIK3CAH1047R-driven tumor growth and PI3K signaling can occur independently of ErbB RTKs. However, simultaneous blockade of p110α and ErbB RTKs results in superior inhibition of PI3K and mammary tumor growth, suggesting a rational therapeutic combination against breast cancers harboring PIK3CA activating mutations

Efferocytosis produces a prometastatic landscape during postpartum mammary gland involution

Breast cancers that occur in women 2–5 years postpartum are more frequently diagnosed at metastatic stages and correlate with poorer outcomes compared with breast cancers diagnosed in young, premenopausal women. The molecular mechanisms underlying the malignant severity associated with postpartum breast cancers (ppBCs) are unclear but relate to stromal wound-healing events during postpartum involution, a dynamic process characterized by widespread cell death in milk-producing mammary epithelial cells (MECs). Using both spontaneous and allografted mammary tumors in fully immune–competent mice, we discovered that postpartum involution increases mammary tumor metastasis. Cell death was widespread, not only occurring in MECs but also in tumor epithelium. Dying tumor cells were cleared through receptor tyrosine kinase MerTK–dependent efferocytosis, which robustly induced the transcription of genes encoding wound-healing cytokines, including IL-4, IL-10, IL-13, and TGF-β. Animals lacking MerTK and animals treated with a MerTK inhibitor exhibited impaired efferocytosis in postpartum tumors, a reduction of M2-like macrophages but no change in total macrophage levels, decreased TGF-β expression, and a reduction of postpartum tumor metastasis that was similar to the metastasis frequencies observed in nulliparous mice. Moreover, TGF-β blockade reduced postpartum tumor metastasis. These data suggest that widespread cell death during postpartum involution triggers efferocytosis-induced wound-healing cytokines in the tumor microenvironment that promote metastatic tumor progression