Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions

Targeting the RAS upstream and downstream signaling pathway for cancer treatment

Cancer often involves the overactivation of RAS/RAF/MEK/ERK (MAPK) and PI3K-Akt-mTOR pathways due to mutations in genes like RAS, RAF, PTEN, and PIK3CA. Various strategies are employed to address the overactivation of these pathways, among which targeted therapy emerges as a promising approach. Directly targeting specific proteins, leads to encouraging results in cancer treatment. For instance, RTK inhibitors such as imatinib and afatinib selectively target these receptors, hindering ligand binding and reducing signaling initiation. These inhibitors have shown potent efficacy against Non-Small Cell Lung Cancer. Other inhibitors, like lonafarnib targeting Farnesyltransferase and GGTI 2418 targeting geranylgeranyl Transferase, disrupt post-translational modifications of proteins. Additionally, inhibition of proteins like SOS, SH2 domain, and Ras demonstrate promising anti-tumor activity both in vivo and in vitro. Targeting downstream components with RAF inhibitors such as vemurafenib, dabrafenib, and sorafenib, along with MEK inhibitors like trametinib and binimetinib, has shown promising outcomes in treating cancers with BRAF-V600E mutations, including myeloma, colorectal, and thyroid cancers. Furthermore, inhibitors of PI3K (e.g., apitolisib, copanlisib), AKT (e.g., ipatasertib, perifosine), and mTOR (e.g., sirolimus, temsirolimus) exhibit promising efficacy against various cancers such as Invasive Breast Cancer, Lymphoma, Neoplasms, and Hematological malignancies. This review offers an overview of small molecule inhibitors targeting specific proteins within the RAS upstream and downstream signaling pathways in cancer.

Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies

PURPOSE

Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary.

MATERIALS AND METHODS

We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT.

RESULTS

MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 (P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 (P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 (P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% (P = .0026 [95% CI, 65 to 91]).

CONCLUSION

MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies.

Progress in patient-derived liver cancer cell models: a step forward for precision medicine

The development of effective precision treatments for liver cancers has been hindered by the scarcity of preclinical models that accurately reflect the heterogeneity of this disease. Recent progress in developing patient-derived liver cancer cell lines and organoids has paved the way for precision medicine research. These expandable resources of liver cancer cell models enable a full spectrum of pharmacogenomic analysis for liver cancers. Moreover, patient-derived and short-term cultured two-dimensional tumor cells or three-dimensional organoids can serve as patient avatars, allowing for the prediction of patients' response to drugs and facilitating personalized treatment for liver cancer patients. Furthermore, the current novel techniques have expanded the scope of cancer research, including innovative organoid culture, gene editing and bioengineering. In this review, we provide an overview of the progress in patient-derived liver cancer cell models, focusing on their applications in precision and personalized medicine research. We also discuss the challenges and future perspectives in this field.

May EPH/Ephrin Targeting Revolutionize Lung Cancer Treatment?

Lung cancer (LC) is the leading cause of cancer death in the United States. Erythropoietin-producing hepatocellular receptors (EPHs) comprise the largest receptor tyrosine kinases (RTKs) family in mammals. EPHs along with their ligands, EPH-family receptor-interacting proteins (ephrins), have been found to be either up- or downregulated in LC cells, hence exhibiting a defining role in LC carcinogenesis and tumor progression. In their capacity as membrane-bound molecules, EPHs/ephrins may represent feasible targets in the context of precision cancer treatment. In order to investigate available therapeutics targeting the EPH/ephrin system in LC, a literature review was conducted, using the MEDLINE, LIVIVO, and Google Scholar databases. EPHA2 is the most well-studied EPH/ephrin target in LC treatment. The targeting of EPHA2, EPHA3, EPHA5, EPHA7, EPHB4, EPHB6, ephrin-A1, ephrin-A2, ephrin-B2, and ephrin-B3 in LC cells or xenograft models not only directly correlates with a profound LC suppression but also enriches the effects of well-established therapeutic regimens. However, the sole clinical trial incorporating a NSCLC patient could not describe objective anti-cancer effects after anti-EPHA2 antibody administration. Collectively, EPHs/ephrins seem to represent promising treatment targets in LC. However, large clinical trials still need to be performed, with a view to examining the effects of EPH/ephrin targeting in the clinical setting.

Organoids in lung cancer: A teenager with infinite growth potential

Despite the rapid advancement in lung cancer research, morbidity and mortality remain high in recent years. Therefore, deeper learning of the underlying molecular mechanisms of pathogenesis and discovery of novel effective therapeutic strategies of treatment in lung cancer research are around the corner. Among these, applying an efficient and reliable preclinical model would be a critical step that exists throughout the whole process. Traditional 2D models used in lung cancer research, including lung cancer cell lines and cell-derived xenograft models, cannot recapitulate the situations of patients due to the lack of a tumor microenvironment or tumor heterogeneity. Organoids, newly developed 3D in vitro structures, more comprehensively imitate the architecture, interaction and genetics of human organs. Cancer organoids, especially those derived from individual patients, can better resemble primary tumor tissues and thus have a greater potential for making breakthroughs in future cancer studies. Here, we mainly review recent advances in the methodologies and applications of lung cancer organoids, which are just developing but have huge potential.

The treatment of advanced non-small cell lung cancer harboring KRAS mutation: a new class of drugs for an old target-a narrative review

Background and Objective

The genetic nature of cancer provides the rationale to support the need for molecular diagnosis and patient selection for individualised antineoplastic treatments that are the best in both tolerability and efficacy for each cancer patient, including non-small cell lung cancer (NSCLC) patients. Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations represent the prevalent oncogenic driver in NSCLC, being detected in roughly one-third of cases and KRAS G12C is the most frequent mutation found in approximately 13% of patients.

Methods

This paper gives an overview of the numerous scientific efforts in recent decades aimed at KRAS inhibition.

Key Content and Findings

Sotorasib is the first approved KRAS G12C inhibitor that has been shown to provide a durable clinical benefit in patients with pre-treated NSCLC with KRAS G12C mutation. Together with the development of new targeted drugs, the development of strategies to control resistance mechanisms is one of the major drivers of research that is exploring the use of KRAS inhibitors not only alone, but also in combination with other targeted therapies, chemotherapy and immunotherapy.

Conclusions

This review will describe the major therapeutic developments in KRAS mutation-dependent NSCLC and will analyse future perspectives to maximise benefits for this group of patients.

Cytotoxic activity of KRAS inhibitors in combination with chemotherapeutics

INTRODUCTION

KRAS is the most frequently mutated oncogenic driver in pancreatic, lung, and colon cancer. Recently, KRAS inhibitors in clinical use show promising activity but most responses are partial and drug resistance develops. The use of therapeutics in combination with KRAS inhibitors are expected to improve outcomes.

AREAS COVERED

This review describes the KRAS G12C mutation-specific inhibitors and the SOS1-targeting inhibitors that reduce the GTP-loading of wildtype and mutated KRAS. Both types of compounds reduce tumor cell proliferation in vitro and in vivo. The combinations of the various KRAS inhibitors with downstream signaling effectors, modulators of KRAS-associated metabolic alterations and chemotherapeutics are summarized.

EXPERT OPINION

The clinical potency of mutated KRAS-specific inhibitors needs to be improved by suitable drug combinations. Inhibition of downstream signaling cascades increases toxicity and other combinations exploited comprise G12C-directed inhibitors with SOS1 inhibitors, glucose/glutamine metabolic modulators, classical chemotherapeutics, and others. The most suitable inhibitor combinations corroborated in preclinical development await clinical verification.

RUNX2 Phosphorylation by Tyrosine Kinase ABL Promotes Breast Cancer Invasion

Activity of transcription factors is normally regulated through interaction with other transcription factors, chromatin remodeling proteins and transcriptional co-activators. In distinction to these well-established transcriptional controls of gene expression, we have uncovered a unique activation model of transcription factors between tyrosine kinase ABL and RUNX2, an osteoblastic master transcription factor, for cancer invasion. We show that ABL directly binds to, phosphorylates, and activates RUNX2 through its SH2 domain in a kinase activity-dependent manner and that the complex formation of these proteins is required for expression of its target gene MMP13. Additionally, we show that the RUNX2 transcriptional activity is dependent on the number of its tyrosine residues that are phosphorylated by ABL. In addition to regulation of RUNX2 activity, we show that ABL transcriptionally enhances RUNX2 expression through activation of the bone morphogenetic protein (BMP)-SMAD pathway. Lastly, we show that ABL expression in highly metastatic breast cancer MDA-MB231 cells is associated with their invasive capacity and that ABL-mediated invasion is abolished by depletion of endogenous RUNX2 or MMP13. Our genetic and biochemical evidence obtained in this study contributes to a mechanistic insight linking ABL-mediated phosphorylation and activation of RUNX2 to induction of MMP13, which underlies a fundamental invasive capacity in cancer and is different from the previously described model of transcriptional activation.

Role of the ABL tyrosine kinases in the epithelial-mesenchymal transition and the metastatic cascade

The ABL kinases, ABL1 and ABL2, promote tumor progression and metastasis in various solid tumors. Recent reports have shown that ABL kinases have increased expression and/or activity in solid tumors and that ABL inactivation impairs metastasis. The therapeutic effects of ABL inactivation are due in part to ABL-dependent regulation of diverse cellular processes related to the epithelial to mesenchymal transition and subsequent steps in the metastatic cascade. ABL kinases target multiple signaling pathways required for promoting one or more steps in the metastatic cascade. These findings highlight the potential utility of specific ABL kinase inhibitors as a novel treatment paradigm for patients with advanced metastatic disease. Video abstract.

KRAS-Mutant Non-Small Cell Lung Cancer: An Emerging Promisingly Treatable Subgroup

Lung cancer, the leading cause of cancer-related deaths worldwide, can be classified into small cell lung cancer and non-small cell lung cancer (NSCLC). NSCLC is the most common histological type, accounting for 85% of all lung cancers. Kirsten rat sarcoma viral oncogene (KRAS) mutations, common in NSCLC, are associated with poor prognosis, likely due to poor responses to most systemic therapies and lack of targeted drugs. The latest published clinical trial data on new small-molecule KRAS G12C inhibitors, AMG510 and MRTX849, indicate that these molecules may potentially help treat KRAS-mutant NSCLC. Simultaneously, within the immuno-therapeutic process, immune efficacy has been observed in those patients who have KRAS mutations. In this article, the pathogenesis, treatment status, progress of immunotherapy, and targeted therapy of KRAS-mutant NSCLC are reviewed.

The prospects of tumor chemosensitivity testing at the single-cell level

Tumor chemosensitivity testing plays a pivotal role in the optimal selection of chemotherapeutic regimens for cancer patients in a personalized manner. High-throughput drug screening approaches have been developed but they failed to take into account intratumor heterogeneity and therefore only provided limited predictive power of therapeutic response to individual cancer patients. Single cancer cell drug sensitivity testing (SCC-DST) has been recently developed to evaluate the variable sensitivity of single cells to different anti-tumor drugs. In this review, we discuss how SCC-DST overcomes the obstacles of traditional drug screening methodologies. We outline critical procedures of SCC-DST responsible for single-cell generation and sorting, cell-drug encapsulation on a microfluidic chip and detection of cell-drug interactions. In SCC-DST, droplet-based microfluidics is emerging as an important platform that integrated various assays and analyses for drug susceptibility tests for individual patients. With the advancement of technology, both fluorescence imaging and label-free analysis have been used for detecting single cell-drug interactions. We also discuss the feasibility of integrating SCC-DST with single-cell RNA sequencing to unravel the mechanisms leading to drug resistance, and utilizing artificial intelligence to facilitate the analysis of various omics data in the evaluation of drug susceptibility. SCC-DST is setting the stage for better drug selection for individual cancer patients in the era of precision medicine.

Emerging Kinase Therapeutic Targets in Pancreatic Ductal Adenocarcinoma and Pancreatic Cancer Desmoplasia

Kinase drug discovery represents an active area of therapeutic research, with previous pharmaceutical success improving patient outcomes across a wide variety of human diseases. In pancreatic ductal adenocarcinoma (PDAC), innovative pharmaceutical strategies such as kinase targeting have been unable to appreciably increase patient survival. This may be due, in part, to unchecked desmoplastic reactions to pancreatic tumors. Desmoplastic stroma enhances tumor development and progression while simultaneously restricting drug delivery to the tumor cells it protects. Emerging evidence indicates that many of the pathologic fibrotic processes directly or indirectly supporting desmoplasia may be driven by targetable protein tyrosine kinases such as Fyn-related kinase (FRK); B lymphoid kinase (BLK); hemopoietic cell kinase (HCK); ABL proto-oncogene 2 kinase (ABL2); discoidin domain receptor 1 kinase (DDR1); Lck/Yes-related novel kinase (LYN); ephrin receptor A8 kinase (EPHA8); FYN proto-oncogene kinase (FYN); lymphocyte cell-specific kinase (LCK); tec protein kinase (TEC). Herein, we review literature related to these kinases and posit signaling networks, mechanisms, and biochemical relationships by which this group may contribute to PDAC tumor growth and desmoplasia.

Steroid receptor-associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies

BACKGROUND

Steroid receptor-associated and regulated protein (SRARP) has recently been identified as a novel tumor suppressor in malignancies of multiple tissue origins. SRARP is located on chromosome 1p36.13 and is widely inactivated by deletions and epigenetic silencing in malignancies. Therefore, additional studies are required to explore SRARP as a potential cancer biomarker.

AIM

This study explores the application of SRARP as a novel biomarker in malignancies of multiple tissue origins using the analysis of large genomic datasets.

METHODS AND RESULTS

A comprehensive genomic analysis of large cancer datasets was carried out to examine the association of SRARP expression and copy-number with molecular and clinical features in malignancies of multiple tissue origins. This study demonstrated that SRARP under-expression and copy-number loss are strongly associated with the loss of other tumor suppressors such as TP53 and NF1 mutations and oncogenic gains, including N-MYC amplification and ERG rearrangement, suggesting that SRARP inactivation is associated with wider genomic instability in malignancies. Importantly, SRARP under-expression and copy-number loss are strong predictors of poor clinical and/or pathological features in breast, colorectal, lung, prostate, gastric, endometrial, cervical, brain, ovarian, bladder, thyroid, and hepatocellular cancers as well as neuroblastoma, uveal melanoma, and acute myeloid leukemia with highly significant odds ratios. Finally, higher SRARP expression and copy-number predict a better response to several cancer drugs.

CONCLUSION

This study suggests that the SRARP inactivation presents a robust biomarker in predicting molecular and clinicopathological features, and treatment response in malignancies.

Carcinomas with Occult Metastasis Potential: Diagnosis/Prognosis Accuracy Improvement by Means of Force Spectroscopy

Accurate diagnosis of cancer stage is inevitable for the following prognosis in patients struggling with these lesions to promote their health and survival rate. Previous studies on survival rate statistics show, in some cases, failure in cancer stage surveys in which metastasis or recurrence of the disease was not accurately prognosed. Morphology study of cancer cells advances the understanding about cancer behavior and its progression, in which, in our previous study on invasive cancer cells, fewer formations of cytoskeleton components compared to their counterparts was observed. Here it is shown that carcinomas with an occult propensity of metastasis depict a number of poorly differentiated cells with decreased amounts of cytoskeleton components in a near-well differentiated population. Force spectroscopy in conjunction with fluorescence microscopy of lung cancer, liver hepatoma, and melanoma provides a general view of these cells' architecture, leading to the conclusion that the scarce abnormal-shaped cells with low formation of structural filaments convey the high risk of metastatic potential of the tumor. The results demonstrate that force spectroscopy complements conventional diagnostic approaches by an accurate cytoskeleton assessment and can improve the following prognosis in epithelial cancers with occult metastasis risk.

Measurement and models accounting for cell death capture hidden variation in compound response

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response.

YES1 Drives Lung Cancer Growth and Progression and Predicts Sensitivity to Dasatinib

Rationale: The characterization of new genetic alterations is essential to assign effective personalized therapies in non-small cell lung cancer (NSCLC). Furthermore, finding stratification biomarkers is essential for successful personalized therapies. Molecular alterations of YES1, a member of the SRC (proto-oncogene tyrosine-protein kinase Src) family kinases (SFKs), can be found in a significant subset of patients with lung cancer.Objectives: To evaluate YES1 (v-YES-1 Yamaguchi sarcoma viral oncogene homolog 1) genetic alteration as a therapeutic target and predictive biomarker of response to dasatinib in NSCLC.Methods: Functional significance was evaluated by in vivo models of NSCLC and metastasis and patient-derived xenografts. The efficacy of pharmacological and genetic (CRISPR [clustered regularly interspaced short palindromic repeats]/Cas9 [CRISPR-associated protein 9]) YES1 abrogation was also evaluated. In vitro functional assays for signaling, survival, and invasion were also performed. The association between YES1 alterations and prognosis was evaluated in clinical samples.Measurements and Main Results: We demonstrated that YES1 is essential for NSCLC carcinogenesis. Furthermore, YES1 overexpression induced metastatic spread in preclinical in vivo models. YES1 genetic depletion by CRISPR/Cas9 technology significantly reduced tumor growth and metastasis. YES1 effects were mainly driven by mTOR (mammalian target of rapamycin) signaling. Interestingly, cell lines and patient-derived xenograft models with YES1 gene amplifications presented a high sensitivity to dasatinib, an SFK inhibitor, pointing out YES1 status as a stratification biomarker for dasatinib response. Moreover, high YES1 protein expression was an independent predictor for poor prognosis in patients with lung cancer.Conclusions: YES1 is a promising therapeutic target in lung cancer. Our results provide support for the clinical evaluation of dasatinib treatment in a selected subset of patients using YES1 status as predictive biomarker for therapy.

Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity

An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.

Small molecules-Giant leaps for immuno-oncology

Immuno-oncology therapies are revolutionizing the oncology landscape with checkpoint blockade becoming the treatment backbone for many indications. While inspiring, much work remains to increase the number of cancer patients that can benefit from these treatments. Thus, a new era of immuno-oncology research has begun which is focused on identifying novel combination regimes that lead to improved response rates. This review highlights the significance of small molecules in this approach and illustrates the huge progress that has been made to date.

A Pharmacogenomic Landscape in Human Liver Cancers

Liver cancers are highly heterogeneous with poor prognosis and drug response. A better understanding between genetic alterations and drug responses would facilitate precision treatment for liver cancers. To characterize the landscape of pharmacogenomic interactions in liver cancers, we developed a protocol to establish human liver cancer cell models at a success rate of around 50% and generated the Liver Cancer Model Repository (LIMORE) with 81 cell models. LIMORE represented genomic and transcriptomic heterogeneity of primary cancers. Interrogation of the pharmacogenomic landscape of LIMORE discovered unexplored gene-drug associations, including synthetic lethalities to prevalent alterations in liver cancers. Moreover, predictive biomarker candidates were suggested for the selection of sorafenib-responding patients. LIMORE provides a rich resource facilitating drug discovery in liver cancers.

mTORC1 inhibitor RAD001 (everolimus) enhances non-small cell lung cancer cell radiosensitivity in vitro via suppressing epithelial-mesenchymal transition

Resistance to radiotherapy causes non-small cell lung cancer (NSCLC) treatment failure associated with local recurrence and metastasis. Thus, understanding the radiosensitization of NSCLC cells is crucial for developing new treatments and improving prognostics. mTORC1 has been shown to regulate tumor cell radiosensitivity, but the underlying mechanisms are unclear. Moreover, mTORC1 also regulates epithelial-mesenchymal transition (EMT) that is important to metastasis and recurrence. In this study we explored whether mTORC1 regulated NSCLC cell radiosensitivity by altering EMT. We performed immunohistichemical analysis using tumor, adjacent and normal tissues from 50 NSCLC patients, which confirmed significantly elevated mTOR protein expression in NSCLC tissue. Then we used NCI-H460 and NCI-H661 cell lines to examine the effects of the mTORC1 inhibitor RAD001 (everolimus) on in vitro radiosensitivity, protein expression and dose-survival curves. RAD001 (10 nmol/L) significantly inhibited the mTORC1 pathway in both the cell lines. Pretreatment with RAD001 (0.1 nmol/L) enhanced the radiosensitivity in NCI-H661 cells with wild-type PIK3CA and KRAS but not in NCI-H460 cells with mutant PIK3CA and KRAS; the sensitivity enhancement ratios in the two NSCLC cell lines were 1.40 and 1.03, respectively. Furthermore, pretreatment with RAD001 (0.1 nmol/L) significantly decreased the migration and invasion with altered expression of several EMT-associated proteins (significantly increased E-cadherin and decreased vimentin expression) in irradiated NCI-H661 cells. Publicly available expression data confirmed that irradiation affected mTOR and EMT-associated genes at the transcript level in NSCLC cells. These results suggest that mTORC1 inhibition enhances the in vitro radiosensitivity of NSCLC cells with wild-type PIK3CA and KRAS by affecting EMT. Our preclinical data may provide a potential new strategy for NSCLC treatment.

Xenograft and organoid model systems in cancer research

Patient‐derived tumour xenografts and tumour organoids have become important preclinical model systems for cancer research. Both models maintain key features from their parental tumours, such as genetic and phenotypic heterogeneity, which allows them to be used for a wide spectrum of applications. In contrast to patient‐derived xenografts, organoids can be established and expanded with high efficiency from primary patient material. On the other hand, xenografts retain tumour–stroma interactions, which are known to contribute to tumorigenesis. In this review, we discuss recent advances in patient‐derived tumour xenograft and tumour organoid model systems and compare their promises and challenges as preclinical models in cancer research.

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

BACKGROUND

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

METHODS

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.

RESULTS

Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).

CONCLUSIONS

These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Sequential feature selection and inference using multi-variate random forests

Motivation

Random forest (RF) has become a widely popular prediction generating mechanism. Its strength lies in its flexibility, interpretability and ability to handle large number of features, typically larger than the sample size. However, this methodology is of limited use if one wishes to identify statistically significant features. Several ranking schemes are available that provide information on the relative importance of the features, but there is a paucity of general inferential mechanism, particularly in a multi-variate set up. We use the conditional inference tree framework to generate a RF where features are deleted sequentially based on explicit hypothesis testing. The resulting sequential algorithm offers an inferentially justifiable, but model-free, variable selection procedure. Significant features are then used to generate predictive RF. An added advantage of our methodology is that both variable selection and prediction are based on conditional inference framework and hence are coherent.

Results

We illustrate the performance of our Sequential Multi-Response Feature Selection approach through simulation studies and finally apply this methodology on Genomics of Drug Sensitivity for Cancer dataset to identify genetic characteristics that significantly impact drug sensitivities. Significant set of predictors obtained from our method are further validated from biological perspective.

Availability and implementation

https://github.com/jomayer/SMuRF.

Contact

souparno.ghosh@ttu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

In silico signaling modeling to understand cancer pathways and treatment responses

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

Phase 1 trial of dasatinib combined with afatinib for epidermal growth factor receptor- (EGFR-) mutated lung cancer with acquired tyrosine kinase inhibitor (TKI) resistance

BACKGROUND

Bypass activation of Src family kinases can confer resistance to EGFR tyrosine kinase inhibitors (TKIs) based on preclinical models. We prospectively assessed the safety and clinical activity of dasatinib and afatinib in combination for patients with resistant EGFR-mutant lung cancer.

METHODS

An open-label, dose-escalation phase 1/2 trial (NCT01999985) with 2-stage expansion was conducted with 25 lung cancer patients. Dose expansion required activating EGFR mutations and progression following prior EGFR TKI.

RESULTS

Patients were 72% Caucasian and received median of 2 prior lines of therapy. Maximum-tolerated dose was 30 mg afatinib with 100 mg dasatinib. New or increased pleural effusions were observed in 56% of patients. No radiologic responses were observed, although several EGFR-mutant TKI-resistant patients (26%) had prolonged stable disease over 6 months. The combination reduced the EGFR mutation and T790M variant allele frequency in cell-free DNA (p < .05). Nonetheless, the threshold for futility was met, based on 6-month progression-free survival. For EGFR TKI-resistant patients, median progression-free survival was 3.7 months (95% confidence interval (CI), 2.3-5.0) and overall survival was 14.7 months (95% CI, 8.5-20.9).

CONCLUSIONS

The combination had a manageable toxicity profile and in vivo T790M modulation, but no objective clinical responses were observed.

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

Recent work points to a lack of diversity in genomics studies from genome-wide association studies to somatic (tumor) genome analyses. Yet, population-specific genetic variation has been shown to contribute to health disparities in cancer risk and outcomes. Immortalized cancer cell lines are widely used in cancer research, from mechanistic studies to drug screening. Larger collections of cancer cell lines better represent the genomic heterogeneity found in primary tumors. Yet, the genetic ancestral origin of cancer cell lines is rarely acknowledged and often unknown. Using genome-wide genotyping data from 1,393 cancer cell lines from the Catalogue of Somatic Mutations in Cancer (COSMIC) and Cancer Cell Line Encyclopedia (CCLE), we estimated the genetic ancestral origin for each cell line. Our data indicate that cancer cell line collections are not representative of the diverse ancestry and admixture characterizing human populations. We discuss the implications of genetic ancestry and diversity of cellular models for cancer research and present an interactive tool, Estimated Cell Line Ancestry (ECLA), where ancestry can be visualized with reference populations of the 1000 Genomes Project. Cancer researchers can use this resource to identify cell line models for their studies by taking ancestral origins into consideration.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Machine learning has identified genetic signatures that predict how patients will respond to three of the most widely used cancer drugs. Chemotherapy regimens are usually based on how groups of people with similar cancers respond to them, but genetic differences can render the drugs more or less effective in individual patients. Machine learning provides a way of sifting through large amounts of data to identify patterns—in this case, in gene signatures associated with cancer recurrence and remission. The authors investigated cellular responses to cisplatin, carboplatin, and oxaliplatin and identified signatures in 11–15 genes which were the most predictive for each drug. The compositions of these signatures are also tailored to how well these therapies prevent growth of cancer cells. Accuracy varied, but one cisplatin signature was able to predict all instances of disease recurrence in non-smokers with bladder cancer.

Cell-Based Methods for Determination of Efficacy for Candidate Therapeutics in the Clinical Management of Cancer

Determination of therapeutic efficacy is a major challenge in developing treatment options for cancer. Prior to in vivo studies, candidate therapeutics are evaluated using cell-based in vitro methods to assess their anti-cancer potential. This review describes the utility and limitations of evaluating therapeutic efficacy using human tumor-derived cell lines. Indicators for therapeutic efficacy using tumor-derived cell lines include cell viability, cell proliferation, colony formation, cytotoxicity, cytostasis, induction of apoptosis, and cell cycle arrest. Cell panel screens, 3D tumor spheroid models, drug-drug/drug-radiation combinatorial analysis, and invasion/migration assays reveal analogous in vitro information. In animal models, cellular assays can assess tumor micro-environment and therapeutic delivery. The utility of tumor-derived cell lines for efficacy determination is manifest in numerous commercially approved drugs that have been applied in clinical management of cancer. Studies reveal most tumor-derived cell lines preserve the genomic signature of the primary tumor source and cell line-based data is highly predictive of subsequent clinical studies. However, cell-based data often disregards natural system components, resulting in cell autonomous outcomes. While 3D cell culture platforms can counter such limitations, they require additional time and cost. Despite the limitations, cell-based methods remain essential in early stages of anti-cancer drug development.

Mutations, protein homeostasis, and epigenetic control of genome integrity

From bacteria to humans, ancient stress responses enable organisms to contend with damage to both the genome and the proteome. These pathways have long been viewed as fundamentally separate responses. Yet recent discoveries from multiple fields have revealed surprising links between the two. Many DNA-damaging agents also target proteins, and mutagenesis induced by DNA damage produces variant proteins that are prone to misfolding, degradation, and aggregation. Likewise, recent studies have observed pervasive engagement of a p53-mediated response, and other factors linked to maintenance of genomic integrity, in response to misfolded protein stress. Perhaps most remarkably, protein aggregation and self-assembly has now been observed in multiple proteins that regulate the DNA damage response. The importance of these connections is highlighted by disease models of both cancer and neurodegeneration, in which compromised DNA repair machinery leads to profound defects in protein quality control, and vice versa.

Genome-wide haplotype association study identifies risk genes for non-small cell lung cancer

Lung cancer is the leading cause of cancer-related death worldwide. Most lung cancer is non-small cell lung cancer (NSCLC), in which malignant cells form in the lung epithelium. Mutations in multiple genes and environmental factors both contribute to NSCLC, and although some NSCLC susceptibility genes have been characterized, the pathogenesis of this disease remains unclear. To identify genes conferring NSCLC risk and determine their associated pathological mechanism, we combined genome-wide haplotype associated analysis with gene prioritization using 224,677 SNPs in 37 NSCLC cell lines and 116 unrelated European individuals. Five candidate genes were identified: ESR1, TGFBR1, INSR, CDH3, and MAP3K5. All of these have previously been implicated in NSCLC, with the exception of CDH3, which can therefore be considered a novel indicator of NSCLC risk. Functional annotation confirmed the relationship between these five genes and NSCLC. Our findings are indicative of the underlying pathological mechanisms of NSCLC and provide information to support future directions in diagnosing and treating NSCLC.

Targeting an oncogenic kinase/phosphatase signaling network for cancer therapy

Protein kinases and phosphatases signal by phosphorylation and dephosphorylation to precisely control the activities of their individual and common substrates for a coordinated cellular outcome. In many situations, a kinase/phosphatase complex signals dynamically in time and space through their reciprocal regulations and their cooperative actions on a substrate. This complex may be essential for malignant transformation and progression and can therefore be considered as a target for therapeutic intervention. p38γ is a unique MAPK family member that contains a PDZ motif at its C-terminus and interacts with a PDZ domain-containing protein tyrosine phosphatase PTPH1. This PDZ-coupled binding is required for both PTPH1 dephosphorylation and inactivation of p38γ and for p38γ phosphorylation and activation of PTPH1. Moreover, the p38γ/PTPH1 complex can further regulate their substrates phosphorylation and dephosphorylation, which impacts Ras transformation, malignant growth and progression, and therapeutic response. This review will use the p38γ/PTPH1 signaling network as an example to discuss the potential of targeting the kinase/phosphatase signaling complex for development of novel targeted cancer therapy.

A combined tissue-engineered/in silico signature tool patient stratification in lung cancer

Patient‐tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix‐based three‐dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR‐activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho‐arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho‐kinase array, proliferation, and apoptosis), we generated cell line‐specific in silico topologies and condition‐specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS‐mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.

Systematic Kinase Inhibitor Profiling Identifies CDK9 as a Synthetic Lethal Target in NUT Midline Carcinoma

Kinase inhibitors represent the backbone of targeted cancer therapy, yet only a limited number of oncogenic drivers are directly druggable. By interrogating the activity of 1,505 kinase inhibitors, we found that BRD4-NUT-rearranged NUT midline carcinoma (NMC) cells are specifically killed by CDK9 inhibition (CDK9i) and depend on CDK9 and Cyclin-T1 expression. We show that CDK9i leads to robust induction of apoptosis and of markers of DNA damage response in NMC cells. While both CDK9i and bromodomain inhibition over time result in reduced Myc protein expression, only bromodomain inhibition induces cell differentiation and a p21-induced cell-cycle arrest in these cells. Finally, RNA-seq and ChIP-based analyses reveal a BRD4-NUT-specific CDK9i-induced perturbation of transcriptional elongation. Thus, our data provide a mechanistic basis for the genotype-dependent vulnerability of NMC cells to CDK9i that may be of relevance for the development of targeted therapies for NMC patients.

YAP and TAZ in Lung Cancer: Oncogenic Role and Clinical Targeting

Lung cancer is the leading cause of cancer death in the world and there is no current treatment able to efficiently treat the disease as the tumor is often diagnosed at an advanced stage. Moreover, cancer cells are often resistant or acquire resistance to the treatment. Further knowledge of the mechanisms driving lung tumorigenesis, aggressiveness, metastasization, and resistance to treatments could provide new tools for detecting the disease at an earlier stage and for a better response to therapy. In this scenario, Yes Associated Protein (YAP) and Trascriptional Coactivator with PDZ-binding motif (TAZ), the final effectors of the Hippo signaling transduction pathway, are emerging as promising therapeutic targets. Here, we will discuss the most recent advances made in YAP and TAZ biology in lung cancer and, more importantly, on the newly discovered mechanisms of YAP and TAZ inhibition in lung cancer as well as their clinical implications.

Mechanoresponsive stem cells to target cancer metastases through biophysical cues

Despite decades of effort, little progress has been made to improve the treatment of cancer metastases. To leverage the central role of the mechanoenvironment in cancer metastasis, we present a mechanoresponsive cell system (MRCS) to selectively identify and treat cancer metastases by targeting the specific biophysical cues in the tumor niche in vivo. Our MRCS uses mechanosensitive promoter-driven mesenchymal stem cell (MSC)-based vectors, which selectively home to and target cancer metastases in response to specific mechanical cues to deliver therapeutics to effectively kill cancer cells, as demonstrated in a metastatic breast cancer mouse model. Our data suggest a strong correlation between collagen cross-linking and increased tissue stiffness at the metastatic sites, where our MRCS is specifically activated by the specific cancer-associated mechano-cues. MRCS has markedly reduced deleterious effects compared to MSCs constitutively expressing therapeutics. MRCS indicates that biophysical cues, specifically matrix stiffness, are appealing targets for cancer treatment due to their long persistence in the body (measured in years), making them refractory to the development of resistance to treatment. Our MRCS can serve as a platform for future diagnostics and therapies targeting aberrant tissue stiffness in conditions such as cancer and fibrotic diseases, and it should help to elucidate mechanobiology and reveal what cells "feel" in the microenvironment in vivo.

A Phase Ib/II Study of Ganetespib With Doxorubicin in Advanced Solid Tumors Including Relapsed-Refractory Small Cell Lung Cancer

Introduction

Small cell lung cancer (SCLC) accounts for 15% of all lung cancers and is characterized by high response rates to cytotoxic chemotherapy and equally high rates of relapse. Many resistance mechanisms have been proposed including resistance to doxorubicin via induction of a heat shock response. Ganetespib is a novel and potent non-geldanamycin heat shock protein 90 (Hsp90) inhibitor. In preclinical studies, synergy between ganetespib and doxorubicin was shown. We conducted a phase Ib/II study of the safety, tolerability, and preliminary efficacy of the combination of ganetespib and doxorubicin.

Methods

Patients eligible for the phase Ib portion had advanced tumors that would be appropriate for doxorubicin therapy and those in the phase II portion had relapsed or refractory SCLC. All patients had an ECOG performance status, 0–1 and adequate organ function, including a cardiac ejection fraction ≥50%. Patients who received a lifetime cumulative doxorubicin dose of >150 mg/m² or who had symptomatic brain metastases were excluded. Patients received ganetespib on Days 1 and 8 and doxorubicin 50 mg/m² on day 1 in 21-day cycles.

Results

Eleven patients were enrolled including nine in the phase Ib dose escalation and two in the phase II expansion. The study was terminated by the sponsor. The dose recommended for future study is ganetespib 150 mg/m² in combination with doxorubicin at a dose of 50 mg/m². The most common adverse events of the combination were grade 1/2 diarrhea, nausea, fatigue, and transaminitis. No dose limiting toxicities were observed. Response rate was 25% and median duration of response was 137 days.

Conclusion

Ganetespib plus doxorubicin was a well-tolerated combination and there remains potential for the clinical development of Hsp90 inhibitors in SCLC.

Clinical Trial Registration

https://ClinicalTrials.gov/ct2/show/NCT02261805, identifier NCT02261805.

Prospective identification of resistance mechanisms to HSP90 inhibition in KRAS mutant cancer cells

Inhibition of the HSP90 chaperone results in depletion of many signaling proteins that drive tumorigenesis, such as downstream effectors of KRAS, the most commonly mutated human oncogene. As a consequence, several small-molecule HSP90 inhibitors are being evaluated in clinical trials as anticancer agents. To prospectively identify mechanisms through which HSP90-dependent cancer cells evade pharmacologic HSP90 blockade, we generated multiple mutant KRAS-driven cancer cell lines with acquired resistance to the purine-scaffold HSP90 inhibitor PU-H71. All cell lines retained dependence on HSP90 function, as evidenced by sensitivity to short hairpin RNA-mediated suppression of HSP90AA1 or HSP90AB1 (also called HSP90α and HSP90β, respectively), and exhibited two types of genomic alterations that interfere with the effects of PU-H71 on cell viability and proliferation: (i) a Y142N missense mutation in the ATP-binding domain of HSP90α that co-occurred with amplification of the HSP90AA1 locus, (ii) genomic amplification and overexpression of the ABCB1 gene encoding the MDR1 drug efflux pump. In support of a functional role for these alterations, exogenous expression of HSP90α Y142N conferred PU-H71 resistance to HSP90-dependent cells, and pharmacologic MDR1 inhibition with tariquidar or lowering ABCB1 expression restored sensitivity to PU-H71 in ABCB1-amplified cells. Finally, comparison with structurally distinct HSP90 inhibitors currently in clinical development revealed that PU-H71 resistance could be overcome, in part, by ganetespib (also known as STA9090) but not tanespimycin (also known as 17-AAG). Together, these data identify potential mechanisms of acquired resistance to small molecules targeting HSP90 that may warrant proactive screening for additional HSP90 inhibitors or rational combination therapies.

LYN expression predicts the response to dasatinib in a subpopulation of lung adenocarcinoma patients

Therapies targeting SRC family kinases (SFKs) have shown efficacy in treating non-small cell lung cancer (NSCLC). However, recent clinical trials have found that the SFK inhibitor dasatinib is ineffective in some patient cohorts. Regardless, dasatinib treatment may benefit some NSCLC patient subgroups. Here, we investigated whether expression of LYN, a member of the SFK family, is associated with patient survival, the efficacy of dasatinib, and/or NSCLC cell viability. LYN expression was associated with poor overall survival in a multivariate analysis, and this association was strongest in non-smoker female patients with adenocarcinoma (ADC). In lung ADC cells, LYN expression enhanced cell proliferation, migration, and invasion. Dasatinib inhibited LYN activity and decreased cell viability in LYN-positive ADC cell lines and xenografts. Additionally, we identified the SFKs SRC and YES as candidate dasatinib targets in LYN-negative ADC cell lines. Our findings suggest that LYN is a useful prognostic marker and a selective target of dasatinib therapy in the lung ADC subpopulation especially in female non-smokers with lung ADC.

Implications of KRAS mutations in acquired resistance to treatment in NSCLC

Rationale

KRAS is the most common and, simultaneously, the most ambiguous oncogene implicated in human cancer. Despite KRAS mutations were identified in Non Small Cell Lung Cancers (NSCLCs) more than 20 years ago, selective and specific inhibitors aimed at directly abrogating KRAS activity are not yet available. Nevertheless, many therapeutic approaches have been developed potentially useful to treat NSCLC patients mutated for KRAS and refractory to both standard chemotherapy and targeted therapies.

The focus of this review will be to provide an overview of the network related to the intricate molecular KRAS pathways, stressing on preclinical and clinical studies that investigate the predictive value of KRAS mutations in NSCLC patients.

Materials and Methods

A bibliographic search of the Medline database was conducted for articles published in English, with the keywords KRAS, KRAS mutations in non-small cell lung cancer, KRAS and tumorigenesis, KRAS and TKIs, KRAS and chemotherapy, KRAS and monoclonal antibody, KRAS and immunotherapy, KRAS and drugs, KRAS and drug resistance.

Mechanisms of Primary Drug Resistance in FGFR1-Amplified Lung Cancer

Purpose: The 8p12-p11 locus is frequently amplified in squamous cell lung cancer (SQLC); the receptor tyrosine kinase fibroblast growth factor receptor 1 (FGFR1) being one of the most prominent targets of this amplification. Thus, small molecules inhibiting FGFRs have been employed to treat FGFR1-amplified SQLC. However, only about 11% of such FGFR1-amplified tumors respond to single-agent FGFR inhibition and several tumors exhibited insufficient tumor shrinkage, compatible with the existence of drug-resistant tumor cells.Experimental Design: To investigate possible mechanisms of resistance to FGFR inhibition, we studied the lung cancer cell lines DMS114 and H1581. Both cell lines are highly sensitive to three different FGFR inhibitors, but exhibit sustained residual cellular viability under treatment, indicating a subpopulation of existing drug-resistant cells. We isolated these subpopulations by treating the cells with constant high doses of FGFR inhibitors.Results: The FGFR inhibitor-resistant cells were cross-resistant and characterized by sustained MAPK pathway activation. In drug-resistant H1581 cells, we identified NRAS amplification and DUSP6 deletion, leading to MAPK pathway reactivation. Furthermore, we detected subclonal NRAS amplifications in 3 of 20 (15%) primary human FGFR1-amplified SQLC specimens. In contrast, drug-resistant DMS114 cells exhibited transcriptional upregulation of MET that drove MAPK pathway reactivation. As a consequence, we demonstrate that rational combination therapies resensitize resistant cells to treatment with FGFR inhibitors.Conclusions: We provide evidence for the existence of diverse mechanisms of primary drug resistance in FGFR1-amplified lung cancer and provide a rational strategy to improve FGFR inhibitor therapies by combination treatment. Clin Cancer Res; 23(18); 5527-36. ©2017 AACR.

Drugging the catalytically inactive state of RET kinase in RET-rearranged tumors

Oncogenic fusion events have been identified in a broad range of tumors. Among them, RET rearrangements represent distinct and potentially druggable targets that are recurrently found in lung adenocarcinomas. We provide further evidence that current anti-RET drugs may not be potent enough to induce durable responses in such tumors. We report that potent inhibitors, such as AD80 or ponatinib, that stably bind in the DFG-out conformation of RET may overcome these limitations and selectively kill RET-rearranged tumors. Using chemical genomics in conjunction with phosphoproteomic analyses in RET-rearranged cells, we identify the CCDC6-RETI788N mutation and drug-induced mitogen-activated protein kinase pathway reactivation as possible mechanisms by which tumors may escape the activity of RET inhibitors. Our data provide mechanistic insight into the druggability of RET kinase fusions that may be of help for the development of effective therapies targeting such tumors.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Time since start of first-line therapy as a predictive clinical marker for nintedanib in patients with previously treated non-small cell lung cancer

Introduction

No predictive clinical or genetic markers have been identified or validated for antiangiogenic agents in lung cancer. We aimed to identify a predictive clinical marker of benefit for nintedanib, an angiokinase inhibitor, using data from two large second-line non-small cell lung cancer Phase III trials (LUME-Lung 1 ([LL1] and LUME-Lung 2).

Methods

Predictive marker identification was conducted in a multi-step process using data from both trials; a hypothesis was generated, confirmed and validated. Statistical analyses included a stepwise selection approach, a recursive partitioning method and the evaluation of HRs, including treatment-by-covariate interactions. The marker was finally validated using a prospectively defined hierarchical testing procedure and treatment-by-covariate interaction for overall survival (OS) based on LL1.

Results

Time since start of first-line therapy (TSFLT) was identified as the only predictive clinical marker. A cut-off of 9 months was chosen for further analysis, based on HRs and recursive partitioning. The prospectively defined final validation using OS data from LL1 established the strong relationship between TSFLT and treatment with nintedanib. Patients with adenocarcinoma with TSFLT <9 months showed a greater survival benefit (median OS 10.9 vs 7.9 months, HR 0.75 [95% CI 0.60–0.92]; p=0.0073) compared with patients in the TSFLT >9 months group (median OS 17.0 vs 15.1 months, HR 0.89 [95% CI 0.66–1.19]).

Conclusions

Patients with shorter TSFLT derive a greater progression-free survival and OS benefit from nintedanib. This clinical marker could be used for patient selection and further investigation is warranted regarding pathways promoting aggressive tumour growth and antiangiogenic tyrosine kinase inhibitor benefit.

Constitutively active ABL family kinases, TEL/ABL and TEL/ARG, harbor distinct leukemogenic activities in vivo

ABL (ABL1) and ARG (ABL2) are highly homologous to each other in overall domain structure and amino-acid sequence, with the exception of their C termini. As with ABL, translocations that fuse ARG to ETV6/TEL have been identified in patients with leukemia. To assess the in vivo leukemogenic activity of constitutively active ABL and ARG, we generated a bone marrow (BM) transplantation model using the chimeric forms TEL/ABL and TEL/ARG, which have comparable kinase activities. TEL/ABL rapidly induced fatal myeloid leukemia in recipient mice, whereas recipients of TEL/ARG-transduced cells did not develop myeloid leukemia, instead, they succumbed to a long-latency infiltrative mastocytosis that could be adoptively transferred to secondary recipients. Swapping of the C termini of ABL and ARG altered disease latency and phenotypes. In a detailed in vitro study, TEL/ARG strongly promoted mast cell differentiation in response to stem cell factor or interleukin-3, whereas TEL/ABL preferentially induced myeloid differentiation of hematopoietic stem/progenitor cells. These results indicate that ABL and ARG kinase activate distinct differentiation pathways to induce specific diseases in vivo, that is, myeloid leukemia and mastocytosis, respectively. Further elucidation of the differences in their properties should provide important insight into the pathogenic mechanisms of oncogenes of the ABL kinase family.

Ex vivo tumor culture systems for functional drug testing and therapy response prediction

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional ex vivo assays requires model systems derived from viable tumor samples, which should closely resemble in vivo tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental ‘cancer-on-chip’ procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor–stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

Selection of the right therapy for individual cancer patients is very important with the expanding number of possible treatments. How tumors respond to a therapy can be tested by treating a sample from the tumor outside the body. Various culture methods can be used to maintain this tumor sample. Each of these model systems has its own benefits and disadvantages. In this review, we discuss the advantages and drawbacks of the available model systems and how they can be used to guide personalized medicine.

Dual Inhibition of EGFR and c-Src by Cetuximab and Dasatinib Combined with FOLFOX Chemotherapy in Patients with Metastatic Colorectal Cancer

Purpose: Aberrant activation of the intracellular tyrosine kinase Src has been implicated as a mechanism of acquired chemotherapy resistance in metastatic colorectal cancer (mCRC). Here, the oral tyrosine kinase Src inhibitor, dasatinib, was investigated in combination with FOLFOX and cetuximab.Experimental Design: We performed a phase IB/II study of 77 patients with previously treated mCRC. Primary objectives were to determine the maximum tolerated dose, dose-limiting toxicities (DLT), pharmacodynamics, and efficacy. Using a 3 + 3 design, patients received FOLFOX6 with cetuximab and escalating doses of dasatinib (100, 150, 200 mg daily), followed by a 12-patient expansion cohort at 150 mg. Phase II studies evaluated FOLFOX plus dasatinib 100 mg in KRAS c12/13^mut patients or in combination with cetuximab if KRAS c12/13^WT FAK and paxillin were utilized as surrogate blood biomarkers of Src inhibition, and paired biopsies of liver metastases were obtained in patients in the expansion cohort.Results: In phase IB, the DLTs were grade 3/4 fatigue (20%) and neutropenia (23%). In phase II, grade 3/4 fatigue (23%) and pleural effusions (11%) were present. Response rates were 20% (6 of 30) in the phase IB escalation and expansion cohort and 13% (3 of 24) and 0% (0 of 23) in the KRAS c12/13^WT and mutant cohorts of phase II, respectively. Median progression-free survival was 4.6, 2.3, and 2.3 months, respectively. There was no evidence of Src inhibition based on surrogate blood biomarkers or paired tumor biopsies.Conclusions: The combination of dasatinib plus FOLFOX with or without cetuximab showed only modest clinical activity in refractory colorectal cancer. This appears to be primarily due to a failure to fully inhibit Src at the achievable doses of dasatinib. The combination of dasatinib plus FOLFOX with or without cetuximab did not show meaningful clinical activity in refractory colorectal cancer due to failure to fully inhibit Src. Clin Cancer Res; 23(15); 4146-54. ©2017 AACR.

Inactivation of ABL kinases suppresses non-small cell lung cancer metastasis

Current therapies to treat non-small cell lung carcinoma (NSCLC) have proven ineffective owing to transient, variable, and incomplete responses. Here we show that ABL kinases, ABL1 and ABL2, promote metastasis of lung cancer cells harboring EGFR or KRAS mutations. Inactivation of ABL kinases suppresses NSCLC metastasis to brain and bone, and other organs. ABL kinases are required for expression of prometastasis genes. Notably, ABL1 and ABL2 depletion impairs extravasation of lung adenocarcinoma cells into the lung parenchyma. We found that ABL-mediated activation of the TAZ and β-catenin transcriptional coactivators is required for NSCLC metastasis. ABL kinases activate TAZ and β-catenin by decreasing their interaction with the β-TrCP ubiquitin ligase, leading to increased protein stability. High-level expression of ABL1, ABL2, and a subset of ABL-dependent TAZ- and β-catenin-target genes correlates with shortened survival of lung adenocarcinoma patients. Thus, ABL-specific allosteric inhibitors might be effective to treat metastatic lung cancer with an activated ABL pathway signature.