Functional signaling pathway analysis of lung adenocarcinomas identifies novel therapeutic targets for KRAS mutant tumors

Little is known about the complex signaling architecture of KRAS and the interconnected RAS-driven protein-protein interactions, especially as it occurs in human clinical specimens. This study explored the activated and interconnected signaling network of KRAS mutant lung adenocarcinomas (AD) to identify novel therapeutic targets. Thirty-four KRAS mutant (MT) and twenty-four KRAS wild-type (WT) frozen biospecimens were obtained from surgically treated lung ADs. Samples were subjected to Laser Capture Microdissection and Reverse Phase Protein Microarray analysis to explore the expression/activation levels of 150 signaling proteins along with coactivation concordance mapping. An independent set of 90 non-small cell lung cancers (NSCLC) was used to validate selected findings by immunohistochemistry (IHC). Compared to KRAS WT tumors, the signaling architecture of KRAS MT ADs revealed significant interactions between KRAS downstream substrates, the AKT/mTOR pathway, and a number of Receptor Tyrosine Kinases (RTK). Approximately one-third of the KRAS MT tumors had ERK activation greater than the WT counterpart (p < 0.01). Notably 18% of the KRAS MT tumors had elevated activation of the Estrogen Receptor alpha (ER-α) (p=0.02).This finding was verified in an independent population by IHC (p=0.03). KRAS MT lung ADs appear to have a more intricate RAS linked signaling network than WT tumors with linkage to many RTKs and to the AKT-mTOR pathway. Combination therapy targeting different nodes of this network may be necessary to treat this group of patients. In addition, for patients with KRAS MT tumors and activation of the ER-α, anti-estrogen therapy may have important clinical implications

Concurrent Inhibition of IGF1R and ERK Increases Pancreatic Cancer Sensitivity to Autophagy Inhibitors

The aggressive nature of pancreatic ductal adenocarcinoma (PDAC) mandates the development of improved therapies. As KRAS mutations are found in 95% of PDAC and are critical for tumor maintenance, one promising strategy involves exploiting KRAS-dependent metabolic perturbations. The macrometabolic process of autophagy is upregulated in KRAS-mutant PDAC, and PDAC growth is reliant on autophagy. However, inhibition of autophagy as monotherapy using the lysosomal inhibitor hydroxychloroquine (HCQ) has shown limited clinical efficacy. To identify strategies that can improve PDAC sensitivity to HCQ, we applied a CRISPR-Cas9 loss-of-function screen and found that a top sensitizer was the receptor tyrosine kinase (RTK) insulin-like growth factor 1 receptor (IGF1R). Additionally, reverse phase protein array pathway activation mapping profiled the signaling pathways altered by chloroquine (CQ) treatment. Activating phosphorylation of RTKs, including IGF1R, was a common compensatory increase in response to CQ. Inhibition of IGF1R increased autophagic flux and sensitivity to CQ-mediated growth suppression both in vitro and in vivo. Cotargeting both IGF1R and pathways that antagonize autophagy, such as ERK-MAPK axis, was strongly synergistic. IGF1R and ERK inhibition converged on suppression of glycolysis, leading to enhanced dependence on autophagy. Accordingly, concurrent inhibition of IGF1R, ERK, and autophagy induced cytotoxicity in PDAC cell lines and decreased viability in human PDAC organoids. In conclusion, targeting IGF1R together with ERK enhances the effectiveness of autophagy inhibitors in PDAC. Significance: Compensatory upregulation of IGF1R and ERK- MAPK signaling limits the efficacy of autophagy inhibitors chloroquine and hydroxychloroquine, and their concurrent inhibition synergistically increases autophagy dependence and chloroquine sensitivity in pancreatic ductal adenocarcinoma.Peer reviewe

Protein pathway activation mapping of colorectal metastatic progression reveals metastasis-specific network alterations

The mechanism by which tissue microecology influences invasion and metastasis is largely unknown. Recent studies have indicated differences in the molecular architecture of the metastatic lesion compared to the primary tumor, however, systemic analysis of the alterations within the activated protein signaling network has not been described. Using laser capture microdissection, protein microarray technology, and a unique specimen collection of 34 matched primary colorectal cancers (CRC) and synchronous hepatic metastasis, the quantitative measurement of the total and activated/phosphorylated levels of 86 key signaling proteins was performed. Activation of the EGFR-PDGFR-cKIT network, in addition to PI3K/AKT pathway, was found uniquely activated in the hepatic metastatic lesions compared to the matched primary tumors. If validated in larger study sets, these findings may have potential clinical relevance since many of these activated signaling proteins are current targets for molecularly targeted therapeutics. Thus, these findings could lead to liver metastasis specific molecular therapies for CRC. \uc2\ua9 2012 Springer Science+Business Media Dordrecht

Long-Term ERK Inhibition in

Induction of compensatory mechanisms and ERK reactivation has limited the effectiveness of Raf and MEK inhibitors in -mutant cancers. We determined that direct pharmacologic inhibition of ERK suppressed the growth of a subset of -mutant pancreatic cancer cell lines and that concurrent phosphatidylinositol 3-kinase (PI3K) inhibition caused synergistic cell death. Additional combinations that enhanced ERK inhibitor action were also identified. Unexpectedly, long-term treatment of sensitive cell lines caused senescence, mediated in part by MYC degradation and p16 reactivation. Enhanced basal PI3K-AKT-mTOR signaling was associated with de novo resistance to ERK inhibitor, as were other protein kinases identified by kinome-wide siRNA screening and a genetic gain-of-function screen. Our findings reveal distinct consequences of inhibiting this kinase cascade at the level of ERK

Sentinel node biopsy for breast cancer: is it already a standard of care? A survey of current practice in an Italian region

BACKGROUND: Although sentinel node biopsy (SNB) is becoming the standard approach for axillary staging in patients with small breast cancer, criteria for patient selection and some technical aspects of the procedure have yet to be clearly defined. The aim of the present survey was therefore to investigate the way in which SNB is used by general surgeons working in the Veneto region, Italy. METHODS: A 29-item questionnaire regarding various aspects of SNB practice was mailed to surgeons in charge of breast surgery in all the 56 surgical centres of the region. RESULTS: The rate of response to the questionnaire was 82.1% (n = 46); 69.6% (n = 32) of the respondents routinely perform SNB in their clinical practice. Most of the interviewed surgeons (93.5%) expressed the belief that the acceptable false negative rate should be ≤5%. However, among the surgeons who perform SNB, only 34.4% performed more than 20 SNB during the learning phase. Indications are limited to tumours of ≤1 cm by 31.2% (n = 10) of respondents, ≤2 cm by 46.9% (n = 15) and ≤3 cm by 21.9% (n = 7). Almost all respondents (93.7%) agreed that a clinically positive axilla is a contraindication to SNB, while opinions differed widely concerning other potential contraindications. In most of the centres considered, SN identification is undertaken on the day before surgery using a subdermal injection of 30–50 MBq of 99mTc-albumin-nanocolloid followed by lymphoscintigraphy. CONCLUSIONS: SNB is currently performed in the majority of hospitals in the Veneto region. However, the training phase and criteria used for patient selection differ from centre to centre. Certified training courses and shared guidelines are therefore highly desirable

Low-Dose Vertical Inhibition of the RAF-MEK-ERK Cascade Causes Apoptotic Death of KRAS Mutant Cancers

We address whether combinations with a pan-RAF inhibitor (RAFi) would be effective in KRAS mutant pancreatic ductal adenocarcinoma (PDAC). Chemical library and CRISPR genetic screens identify combinations causing apoptotic anti-tumor activity. The most potent combination, concurrent inhibition of RAF (RAFi) and ERK (ERKi), is highly synergistic at low doses in cell line, organoid, and rat models of PDAC, whereas each inhibitor alone is only cytostatic. Comprehensive mechanistic signaling studies using reverse phase protein array (RPPA) pathway mapping and RNA sequencing (RNA-seq) show that RAFi/ERKi induced insensitivity to loss of negative feedback and system failures including loss of ERK signaling, FOSL1, and MYC; shutdown of the MYC transcriptome; and induction of mesenchymal-to-epithelial transition. We conclude that low-dose vertical inhibition of the RAF-MEK-ERK cascade is an effective therapeutic strategy for KRAS mutant PDAC.Peer reviewe

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Phosphorylation, Signaling, and Cancer: Targets and Targeting

After 60 years from the first report of an enzymatic phosphorylation of proteins, protein kinases are well-established key signaling molecules that impact all major biological processes (reviewed in [1, 2]). Protein and lipid kinases fulfill essential roles in many signaling pathways that regulate normal cell functions [1\u20135]. Deregulation of kinase activities leads to a variety of pathologies ranging from cancer to inflammatory diseases, diabetes, infectious diseases, cardiovascular disorders, and cell growth and survival [1, 2, 5\u201311]. A much larger proportion of additional kinases are present in parasites and bacterial, fungal, and viral genomes that are susceptible to exploitation as drug targets [12]. Since many human diseases result from overactivation of protein and lipid kinases due to mutations and/or overexpression, this enzyme class represents an important target for the pharmaceutical industry [6]. Approximately one-third of all protein targets under investigation in the pharmaceutical industry are protein or lipid kinases and to date 33 small molecular weight kinase inhibitors (SMWKIs) and a handful of therapeutic antibodies have been approved for various indications mainly in oncology and many more in various stages of clinical and preclinical development [5]. Kinase inhibitor drugs, which are in clinical trials, target all stages of signal transduction from the receptor protein tyrosine kinases that initiate intracellular signaling, through second-messenger dependent lipid and protein kinases and protein kinases that regulate the cell cycle [10, 13]. While treating chronic phase CML (an almost monogenic disease) with imatinib has been very successful, the treatment of more advanced cancers with kinase inhibitors has proven more difficult due to the heterogeneity of these cancer types as well as due to kinase inhibitor resistance resulting from selection for mutant alleles and/or upregulation of alternative signaling pathways [5, 10]

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset “Lung-PET-CT-Dx”, obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine