Pharmacogenomic approach to identify drug sensitivity in small-cell lung cancer

DRN-CDR: A cancer drug response prediction model using multi-omics and drug features

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

MALAT1 as master regulator of biomarkers predictive of pan-cancer multi-drug resistance in the context of recalcitrant NRAS signaling pathway identified using systems-oriented approach

NRAS, a protein mutated in several cancer types, is involved in key drug resistance mechanisms and is an intractable target. The development of drug resistance is one of the major impediments in targeted therapy. Currently, gene expression data is used as the most predictive molecular profile in pan-cancer drug sensitivity and resistance studies. However, the common regulatory mechanisms that drive drug sensitivity/resistance across cancer types are as yet, not fully understood. We focused on GDSC data on NRAS-mutant pan-cancer cell lines, to pinpoint key signaling targets in direct or indirect associations with NRAS, in order to identify other druggable targets involved in drug resistance. Large-scale gene expression, comparative gene co-expression and protein–protein interaction network analyses were performed on selected drugs inducing drug sensitivity/resistance. We validated our data from cell lines with those obtained from primary tissues from TCGA. From our big data studies validated with independent datasets, protein-coding hub genes FN1, CD44, TIMP1, SNAI2, and SPARC were found significantly enriched in signal transduction, proteolysis, cell adhesion and proteoglycans pathways in cancer as well as the PI3K/Akt-signaling pathway. Further studies of the regulation of these hub/driver genes by lncRNAs revealed several lncRNAs as prominent regulators, with MALAT1 as a possible master regulator. Transcription factor EGR1 may control the transcription rate of MALAT1 transcript. Synergizing these studies, we zeroed in on a pan-cancer regulatory axis comprising EGR1-MALAT1-driver coding genes playing a role. These identified gene regulators are bound to provide new paradigms in pan-cancer targeted therapy, a foundation for precision medicine, through the targeting of these key driver genes in the improvement of multi-drug sensitivity or resistance.

DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC)

The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data

BACKGROUND

Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug-protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene-gene interactions.

RESULTS

In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study.

CONCLUSIONS

The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.

Search of vasopressin analogs with antiproliferative activity on small-cell lung cancer: drug design based on two different approaches

AIM

Development of compounds with therapeutic application requires the interaction of different disciplines. Several tumors express vasopressin (AVP; arginine vasopressin) receptors with contrasting effects depending on receptor subtype. Desmopressin (dDAVP) is an AVP-selective analog with antiproliferative properties. In this work, an evolutionary approach and a rational strategy were applied in order to design novel AVP analogs.

RESULTS

We designed two novel analogs; dDInotocin (dDINT, insect analog), and [V⁴Q⁵]dDAVP, and demonstrated the importance of the dDAVP conformational loop for its antiproliferative activity. [V⁴Q⁵] dDAVP showed major cytostatic effect on lung cancer cells than dDAVP and its cytostatic effect was abolished by V2R blockade.

CONCLUSION

Combination of these strategies could provide the basis for future studies for the development of improved compounds with potential therapeutic applications.

Biomarker identification for statin sensitivity of cancer cell lines

Statins are potent cholesterol reducing drugs that have been shown to reduce tumor cell proliferation in vitro and tumor growth in animal models. Moreover, retrospective human cohort studies demonstrated decreased cancer-specific mortality in patients taking statins. We previously implicated membrane E-cadherin expression as both a marker and mechanism for resistance to atorvastatin-mediated growth suppression of cancer cells; however, a transcriptome-profile-based biomarker signature for statin sensitivity has not yet been reported. Here, we utilized transcriptome data from fourteen NCI-60 cancer cell lines and their statin dose-response data to produce gene expression signatures that identify statin sensitive and resistant cell lines. We experimentally confirmed the validity of the identified biomarker signature in an independent set of cell lines and extended this signature to generate a proposed statin-sensitive subset of tumors listed in the TCGA database. Finally, we predicted drugs that would synergize with statins and found several predicted combination therapies to be experimentally confirmed. The combined bioinformatics-experimental approach described here can be used to generate an initial biomarker signature for anticancer drug therapy.

Reciprocal expression of INSM1 and YAP1 defines subgroups in small cell lung cancer

The majority of small cell lung cancer (SCLC) patients demonstrate initial chemo-sensitivity, whereas a distinct subgroup of SCLC patients, termed chemo-refractory, do not respond to treatment. There is little understanding of how to distinguish these patients prior to disease treatment. Here we used gene expression profiling to stratify SCLC into subgroups and characterized a molecular phenotype that may identify, in part, chemo-refractive SCLC patients. Two subgroups of SCLC were identified in both cell lines and tumors by the reciprocal expression of two genes; INSM1, a neuroendocrine transcription factor, and YAP1, a key mediator of the Hippo pathway. The great majority of tumors expressed INSM1, which was prognostic for increased progression-free survival and associated with chemo-sensitivity in cell lines. YAP1 is expressed in a minority of SCLC tumors and was shown in cell lines to be downstream of the retinoblastoma protein (RB1) and associated with decreased drug sensitivity. RB1 expression in SCLC cell lines sensitizes them to CDK4/6 inhibitors. Wild-type RB1 mutation status, used as a surrogate marker of YAP1 expression, was prognostic for decreased patient survival and increased chemo-refractory tumor response. Thus, the reciprocal expression of INSM1 and YAP1 appears to stratify SCLC into distinct subgroups and may be useful, along with RB1 mutation status, to identify chemo-refractory SCLC patients.

An open-label, phase II study of the polo-like kinase-1 (Plk-1) inhibitor, BI 2536, in patients with relapsed small cell lung cancer (SCLC)

OBJECTIVES

This phase II, open-label study was designed to evaluate the response rate to the polo-like kinase 1 (Plk-1) inhibitor BI 2536 in patients with sensitive-relapsed small cell lung cancer (SCLC). Secondary endpoints included progression-free survival (PFS), overall survival (OS), duration of response, and safety.

MATERIALS AND METHODS

Patients were treated with the recommended phase II dose of 200mg of BI 2536 intravenously every 21days. This was a two-stage design with an early stopping rule in place if responses were not seen in at least 2 of the first 18 enrolled patients.

RESULTS AND CONCLUSION

Twenty-three patients were enrolled in the study and 21 patients were evaluable for response. No responses were observed and all 23 patients have progressed. The median PFS was 1.4 months. Treatment was generally well tolerated and the most frequent adverse events were neutropenia, fatigue, nausea, vomiting, and constipation. BI 2536 is not effective in the treatment of sensitive relapsed SCLC. The criteria for expanding the trial to the second stage were not achieved, and the study was terminated for a lack of efficacy.

Therapies in the pipeline for small-cell lung cancer

INTRODUCTION OR BACKGROUND

Small-cell lung cancer (SCLC) represents ~15% of all cases of lung cancer and is characterized by a rapid tumour doubling time, early onset disease dissemination and high sensitivity to chemotherapy.

SOURCES OF DATA

We searched MEDLINE and OVID databases for articles in English published from January 1980 to February 2015.

AREAS OF AGREEMENT

Platinum-based chemotherapy, thoracic radiotherapy and prophylactic cranial irradiation are standard of care. Benefit from second-line chemotherapy is limited.

AREAS OF CONTROVERSY

The role of platinum/irinotecan chemotherapy in the Western population and the role of maintenance therapies remain to be established.

GROWING POINTS

Knowledge of the biology of SCLC has expanded exponentially and many potential therapeutic targets have been identified.

AREAS TIMELY FOR DEVELOPING RESEARCH

The use of circulating tumour cells can help investigating molecular alterations occurring within tumour cells, understanding drug resistance mechanisms and evaluating new treatments.

Patient-derived xenografts faithfully replicated clinical outcome in a phase II co-clinical trial of arsenic trioxide in relapsed small cell lung cancer

Background

SCLC has limited treatment options and inadequate preclinical models. Promising activity of arsenic trioxide (ASO) recorded in conventional preclinical models of SCLC supported the clinical evaluation of ASO in patients. We assessed the efficacy of ASO in relapsed SCLC patients and in corresponding patient-derived xenografts (PDX).

Methods

Single arm, Simon 2-stage, phase II trial to enroll patients with relapsed SCLC who have failed at least one line of therapy. ASO was administered as an intravenous infusion over 1–2 h daily for 4 days in week 1 and for 2 days in weeks 2–6 of an 8-week cycle. Treatment continued until disease progression. Pretreatment tumor biopsy was employed for PDX generation through direct implantation into subcutaneous pockets of SCID mice without in vitro manipulation and serially propagated for five generations. Ex vivo efficacy of cisplatin (3 mg/kg i.p. weekly) and ASO (3.75 mg/kg i.p. every other day) was tested in PDX representative of platinum sensitive and platinum refractory SCLC.

Results

The best response in 17 evaluable patients was stable disease in 2 (12 %), progressive disease in 15 (88 %) patients and median time-to-progression of seven (range 1–7) weeks. PDX was successfully grown in 5 of 9 (56 %) transplanted biopsy samples. Serially-propagated PDXs preserved characteristic small cell histology and genomic stability confirmed by immunohistochemistry, short tandem repeat (STR) profiling and targeted sequencing. ASO showed in vitro cytotoxicity but lacked in vivo efficacy against SCLC PDX tumor growth.

Conclusions

Cisplatin inhibited growth of PDX derived from platinum-sensitive SCLC but was ineffective against PDX from platinum-refractory SCLC. Strong concordance between clinical and ex vivo effects of ASO and cisplatin in SCLC supports the use of PDX models to prescreen promising anticancer agents prior to clinical testing in SCLC patients.

Trial Registration The study was registered at http://www.clinicaltrials.gov (NCT01470248)

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0861-5) contains supplementary material, which is available to authorized users.

Small cell lung cancer (SCLC): no treatment advances in recent years

Small cell lung cancer (SCLC) is an aggressive malignancy with a distinct natural history and dismal prognosis. Given its predisposition for early dissemination, patients are commonly diagnosed with metastatic disease and chemotherapy is regarded as the cornerstone of approved treatment strategies. However, over the last 30 years there has been a distinct paucity of significant breakthroughs in SCLC therapy. Thus, SCLC is characterized as a recalcitrant neoplasm with limited therapeutic options. By employing well-established research approaches, proven to be efficacious in non-small cell lung cancer (NSCLC), a growing amount of data has shed light on the molecular biology of SCLC and enhanced our knowledge of the "drivers" of tumor cell survival and proliferation. New therapeutic targets have emerged, but no significant improvement in patients' survival has been demonstrated thus far. In a sense, the more we know, the more we fail. Nowadays this is starting to change and methodical research efforts are underway. It is anticipated that the next decade will see a revolution in the treatment of SCLC patients with the application of effective precision medicine and immunotherapy strategies.

Target Identification in Small Cell Lung Cancer via Integrated Phenotypic Screening and Activity-Based Protein Profiling

To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a target-agnostic phenotypic screen of kinase inhibitors with target identification using activity-based protein profiling (ABPP) in which a desthiobiotin-ATP probe was used. We screened 21 SCLC cell lines with known c-MYC amplification status for alterations in viability using a chemical library of 235 small-molecule kinase inhibitors. One screen hit compound was interrogated with ABPP, and, through this approach, we reidentified Aurora kinase B as a critical kinase in MYC-amplified SCLC cells. We next extended the platform to a second compound that had activity in SCLC cell lines lacking c-MYC amplification and identified TANK-binding kinase 1, a kinase that affects cell viability, polo-like kinase-1 signaling, G2-M arrest, and apoptosis in SCLC cells lacking MYC amplification. These results demonstrate that phenotypic screening combined with ABPP can identify key disease drivers, suggesting that this approach, which combines new chemical probes and disease cell screens, has the potential to identify other important targets in other cancer types. Mol Cancer Ther; 15(2); 334-42. ©2016 AACR.

Beyond adenocarcinoma: current treatments and future directions for squamous, small cell, and rare lung cancer histologies

Lung cancer encompasses a diverse spectrum of histologic subtypes. Until recently, the majority of therapeutic advances were limited to the minority of patients with adenocarcinoma. With the advent of comprehensive genomic profiling of squamous and small cell lung cancers, new therapeutic targets have emerged. For squamous tumors, the most promising of these include fibroblast growth factor receptor (FGFR), the phosphatidylinositol 3-kinase (PI3K) pathway, discoidin domain receptor 2 (DDR2), and G1/S checkpoint regulators. In 2014, the antiangiogenic agent ramucirumab was approved for all non-small cell lung cancer (NSCLC) histologies, including squamous tumors. Immunotherapeutic approaches also appear to be promising for these cases. Genomic analysis of small cell lung cancer has revealed a high mutation burden, but relatively few druggable driver oncogenic alterations. Current treatment strategies under investigation are focusing on targeting mitotic, cell cycle, and DNA repair regulation, as well as immunotherapy. Pulmonary neuroendocrine tumors represent a diverse spectrum of diseases that may be treated with somatostatin analogs, cytotoxic agents, and molecularly targeted therapies. Radiolabeled somatostatin analogs and combinations with mammalian target of rapamycin (mTOR) inhibitors also show potential. Large cell neuroendocrine tumors share numerous clinical, pathologic, and molecular features with small cell lung cancer; however, whether they should be treated similarly or according to a NSCLC paradigm remains a matter of debate.