Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

Integrated landscape of plasma metabolism and proteome of patients with post-traumatic deep vein thrombosis

Deep vein thrombosis (DVT) is a leading cause of morbidity and mortality after trauma. Here, we integrate plasma metabolomics and proteomics to evaluate the metabolic alterations and their function in up to 680 individuals with and without DVT after trauma (pt-DVT). We identify 28 metabolites and 2 clinical parameter clusters associated with pt-DVT. Then, we develop a panel of 9 metabolites (hexadecanedioic acid, pyruvic acid, L-Carnitine, serotonin, PE(P-18:1(11Z)/18:2(9Z,12Z)), 3-Hydroxycapric acid, 5,6-DHET, 3-Methoxybenzenepropanoic acid and pentanenitrile) that can predict pt-DVT with high performance, which can be verified in an independent cohort. Furthermore, the integration analysis of metabolomics and proteomics data indicates that the upregulation of glycolysis/gluconeogenesis-TCA cycle may promote thrombosis by regulating ROS levels in red blood cells, suggesting that interfering with this process might be potential therapeutic strategies for pt-DVT. Together, our study comprehensively delineates the metabolic and hematological dysregulations for pt-DVT, and provides potential biomarkers for early detection.

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

Metabolic Profiling as an Approach to Differentiate T-Cell Acute Lymphoblastic Leukemia Cell Lines Belonging to the Same Genetic Subgroup

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive tumor mainly affecting children and adolescents. It is driven by multiple genetic mutations that together define the leukemic phenotype. Interestingly, based on genetic alterations and/or deregulated expression, at least six genetic subgroups have been recognized. The TAL/LMO subgroup is one of the most represented genetic subgroups, characterizing 30-45% of pediatric T-ALL cases. The study of lipid and metabolic profiles is increasingly recognized as a valuable tool for comprehending the development and progression of tumors. In this study, metabolic and lipidomic analysis via LC/MS have been carried out on four T-ALL cell lines belonging to the TAL/LMO subgroup (Jurkat, Molt-4, Molt-16, and CCRF-CEM) to identify new potential metabolic biomarkers and to provide a subclassification of T-ALL cell lines belonging to the same subgroup. A total of 343 metabolites were annotated, including 126 polar metabolites and 217 lipid molecules. The statistical analysis, for both metabolic and lipid profiles, shows significant differences and similarities among the four cell lines. The Molt-4 cell line is the most distant cell line and CCRF-CEM shows a high activity in specific pathways when compared to the other cell lines, while Molt-16 and Jurkat show a similar metabolic profile. Additionally, this study highlighted the pathways that differ in each cell line and the possible enzymes involved using bioinformatic tools, capable of predicting the pathways involved by studying the differences in the metabolic profiles. This experiment offers an approach to differentiate T-ALL cell lines and could open the way to verify and confirm the obtained results directly in patients.

Inhibition of γ-secretase/Notch pathway as a potential therapy for reversing cancer drug resistance

The mechanism of tumor drug resistance is complex and may involve stem cell maintenance, epithelial-mesenchymal transition, the activation of survival signaling pathways, transporter protein expression, and tumor microenvironment remodeling, all of which are linked to γ-secretase/Notch signaling. Increasing evidence has shown that the activation of the γ-secretase/Notch pathway is a key driver of cancer progression and drug resistance development and that γ-secretase inhibitors (GSIs) may be the most promising agents for reversing chemotherapy resistance of tumors by targeting the γ-secretase/Notch pathway. Here, we systematically summarize the roles in supporting γ-secretase/Notch activation-associated transformation of cancer cells into cancer stem cells, promotion of the EMT process, PI3K/Akt, MEK/ERK and NF-κB activation, enhancement of ABC transporter protein expression, and TME alteration in mediating tumor drug resistance. Subsequently, we analyze the mechanism of GSIs targeting the γ-secretase/Notch pathway to reverse tumor drug resistance and propose the outstanding advantages of GSIs in treating breast cancer drug resistance over other tumors. Finally, we emphasize that the development of GSIs for reversing tumor drug resistance is promising.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Notch Signaling in Health and Disease

The Notch signaling pathway, a vital and evolutionarily conserved regulator of cellular processes, intricately shapes both health and disease [...].

Resistance mechanism to Notch inhibition and combination therapy in human T-cell acute lymphoblastic leukemia

Gain-of-function mutations in NOTCH1 are among the most frequent genetic alterations in T-cell acute lymphoblastic leukemia (T-ALL), highlighting the Notch signaling pathway as a promising therapeutic target for personalized medicine. Yet, a major limitation for long-term success of targeted therapy is relapse due to tumor heterogeneity or acquired resistance. Thus, we performed a genome-wide CRISPR-Cas9 screen to identify prospective resistance mechanisms to pharmacological NOTCH inhibitors and novel targeted combination therapies to efficiently combat T-ALL. Mutational loss of phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) causes resistance to Notch inhibition. PIK3R1 deficiency leads to increased PI3K/AKT signaling, which regulates cell cycle and the spliceosome machinery, both at the transcriptional and posttranslational level. Moreover, several therapeutic combinations have been identified, in which simultaneous targeting of the cyclin-dependent kinases 4 and 6 (CDK4/6) and NOTCH proved to be the most efficacious in T-ALL xenotransplantation models.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Notch Partners in the Long Journey of T-ALL Pathogenesis

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological disease that arises from the oncogenic transformation of developing T cells during T-lymphopoiesis. Although T-ALL prognosis has improved markedly in recent years, relapsing and refractory patients with dismal outcomes still represent a major clinical issue. Consequently, understanding the pathological mechanisms that lead to the appearance of this malignancy and developing novel and more effective targeted therapies is an urgent need. Since the discovery in 2004 that a major proportion of T-ALL patients carry activating mutations that turn NOTCH1 into an oncogene, great efforts have been made to decipher the mechanisms underlying constitutive NOTCH1 activation, with the aim of understanding how NOTCH1 dysregulation converts the physiological NOTCH1-dependent T-cell developmental program into a pathological T-cell transformation process. Several molecular players have so far been shown to cooperate with NOTCH1 in this oncogenic process, and different therapeutic strategies have been developed to specifically target NOTCH1-dependent T-ALLs. Here, we comprehensively analyze the molecular bases of the cross-talk between NOTCH1 and cooperating partners critically involved in the generation and/or maintenance and progression of T-ALL and discuss novel opportunities and therapeutic approaches that current knowledge may open for future treatment of T-ALL patients.

An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies

PURPOSE

Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction, remain largely unexplored. Data suggest the DNA damage response (DDR) as a central signaling network that intersects with pathways associated with deregulated addicting oncoproteins with kinase activity in cancer cells.

EXPERIMENTAL

DESIGN: We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models. An NSCLC tissue pipeline combining patient-derived xenografts (PDXs) and ex vivo patient organotypic cultures has been established for treatment responsiveness assessment.

RESULTS

We identified an 'oncogene addiction phosphorylation signature' (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments.

CONCLUSIONS

We propose a score derived from OAPS as a quantitative measure to evaluate oncogene addiction of cancer cell samples. This work underlines the importance of protein phosphorylation assessment for patient stratification in precision oncology and corresponding identification of tumor subtypes sensitive to inhibition of a particular oncogene.

High-throughput DNA methylation analysis in ITP confirms NOTCH1 hypermethylation through the Th1 and Th2 cell differentiation pathways

BACKGROUND

Immune thrombocytopenia (ITP) is a prevalent autoimmune disease with a complex aetiology where DNA methylation changes are becoming triggers.

METHOD

To investigate novel abnormally methylated genes in the pathogenesis of ITP, we performed a high-throughput methylation analysis on 21 ITP patients and 9 normal control samples. We analysed the extent of key methylated genes and their downstream cytokines through Luminex assay or qRT-PCR. Then, bone marrow mononuclear cells were extracted from ITP patients, and decitabine (demethylation drug) was added to the culture medium of cultured cells. qRT-PCR and ELISA were used to detect whether decitabine could effectively affect target genes and related cytokines.

RESULTS

Through the STRING and Metascape databases, hypermethylated NOTCH1 can be identified and can influence ITP by regulating many downstream cytokines through Th1 and Th2 cell differentiation pathways. Compared with those in the normal control group, the expression levels of NOTCH1 and its downstream Th2 cytokines (IL-4, IL-10, and GATA3) were significantly decreased and those of Th1 cytokines (IFN-γ, IL-12, and TNF-α) were significantly increased in the ITP group. Decitabine exerts its demethylation effect, so the expression of NOTCH1 and its related cytokines in the ITP group treated with 100 nM decitabine were significantly reversed.

CONCLUSIONS

Our results suggest that the pathogenesis of ITP may exert its influence on epigenetics through alteration of DNA methylation at regulatory regions of the target NOTCH1 gene in the Th1 and Th2 cell differentiation pathways. At the same time, decitabine may achieve a therapeutic effect on ITP by demethylation.

Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing

In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.

Comparing Efficiency of Lysis Buffer Solutions and Sample Preparation Methods for Liquid Chromatography-Mass Spectrometry Analysis of Human Cells and Plasma

The use of a proper sample processing methodology for maximum proteome coverage and high-quality quantitative data is an important choice to make before initiating a liquid chromatography–mass spectrometry (LC–MS)-based proteomics study. Popular sample processing workflows for proteomics involve in-solution proteome digestion and single-pot, solid-phase-enhanced sample preparation (SP3). We tested them on both HeLa cells and human plasma samples, using lysis buffers containing SDS, or guanidinium hydrochloride. We also studied the effect of using commercially available depletion mini spin columns before SP3, to increase proteome coverage in human plasma samples. Our results show that the SP3 protocol, using either buffer, achieves the highest number of quantified proteins in both the HeLa cells and plasma samples. Moreover, the use of depletion mini spin columns before SP3 results in a two-fold increase of quantified plasma proteins. With additional fractionation, we quantified nearly 1400 proteins, and examined lower-abundance proteins involved in neurodegenerative pathways and mitochondrial metabolism. Therefore, we recommend the use of the SP3 methodology for biological sample processing, including those after depletion of high-abundance plasma proteins.

MET Inhibition Sensitizes Rhabdomyosarcoma Cells to NOTCH Signaling Suppression

Rhabdomyosarcoma (RMS) is a pediatric myogenic soft tissue sarcoma. The Fusion-Positive (FP) subtype expresses the chimeric protein PAX3-FOXO1 (P3F) while the Fusion-Negative (FN) is devoid of any gene translocation. FP-RMS and metastatic FN-RMS are often unresponsive to conventional therapy. Therefore, novel therapeutic approaches are needed to halt tumor progression. NOTCH signaling has oncogenic functions in RMS and its pharmacologic inhibition through γ-secretase inhibitors blocks tumor growth in vitro and in vivo. Here, we show that NOTCH signaling blockade resulted in the up-regulation and phosphorylation of the MET oncogene in both RH30 (FP-RMS) and RD (FN-RMS) cell lines. Pharmacologic inhibition of either NOTCH or MET signaling slowed proliferation and restrained cell survival compared to control cells partly by increasing Annexin V and CASP3/7 activation. Co-treatment with NOTCH and MET inhibitors significantly amplified these effects and enhanced PARP1 cleavage in both cell lines. Moreover, it severely hampered cell migration, colony formation, and anchorage-independent growth compared to single-agent treatments in both cell lines and significantly prevented the growth of FN-RMS cells grown as spheroids. Collectively, our results unveil the overexpression of the MET oncogene by NOTCH signaling targeting in RMS cells and show that MET pathway blockade sensitizes them to NOTCH inhibition.

Phosphoproteomic profiling of T cell acute lymphoblastic leukemia reveals targetable kinases and combination treatment strategies

Protein kinase inhibitors are amongst the most successful cancer treatments, but targetable kinases activated by genomic abnormalities are rare in T cell acute lymphoblastic leukemia. Nevertheless, kinases can be activated in the absence of genetic defects. Thus, phosphoproteomics can provide information on pathway activation and signaling networks that offer opportunities for targeted therapy. Here, we describe a mass spectrometry-based global phosphoproteomic profiling of 11 T cell acute lymphoblastic leukemia cell lines to identify targetable kinases. We report a comprehensive dataset consisting of 21,000 phosphosites on 4,896 phosphoproteins, including 217 kinases. We identify active Src-family kinases signaling as well as active cyclin-dependent kinases. We validate putative targets for therapy ex vivo and identify potential combination treatments, such as the inhibition of the INSR/IGF-1R axis to increase the sensitivity to dasatinib treatment. Ex vivo validation of selected drug combinations using patient-derived xenografts provides a proof-of-concept for phosphoproteomics-guided design of personalized treatments.

Gene Mutations Related to Glucocorticoid Resistance in Pediatric Acute Lymphoblastic Leukemia

OBJECTIVE

To investigate the correlation between gene mutations and glucocorticoid resistance in pediatric acute lymphoblastic leukemia (ALL).

METHODS

A total of 71 children with ALL admitted to our center between September 2019 and September 2021 were enrolled. DNA obtained from bone marrow or peripheral blood samples at initial diagnosis was used for genetic testing via whole exome sequencing. Meanwhile, patient clinical information was collected. Subsequently, the correlations of gene mutations with clinical features and glucocorticoid resistance were analyzed.

RESULTS

Of the 71 children enrolled, 61 (85.9%) had B-cell ALL (B-ALL) and 10 (14.1%) had T-cell ALL (T-ALL). The five genes with the highest mutation frequency in B-ALL were TTN (24.4%), FLT3 (14.6%), TP53 (14.6%), MUC16 (9.8%), and EPPK1 (9.8%). In contrast, those with the highest frequency in T-ALL were NOTCH1 (54.5%), FBXW7 (27.3%), TTN (27.3%), MUC16 (27.3%), and PHF6 (18.2%). Upon statistical analysis, TTN and NOTCH1 mutations were found to be associated with prednisone resistance. Further, TTN and MUC16 mutations were associated with a lower age at diagnosis, and NOTCH1 mutations were associated with T-ALL in female patients. Leukocyte counts and LDH levels did not differ based on the presence of any common gene mutation, and no association between these gene mutations and overall survival was observed.

CONCLUSIONS

Our study is the first to demonstrate the association between TTN mutation and glucocorticoid resistance in ALL. Our findings could guide strategies for overcoming drug resistance and aid in the development of drug targets.

Biologic and Therapeutic Implications of Genomic Alterations in Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most successful paradigm of how risk-adapted therapy and detailed understanding of the genetic alterations driving leukemogenesis and therapeutic response may dramatically improve treatment outcomes, with cure rates now exceeding 90% in children. However, ALL still represents a leading cause of cancer-related death in the young, and the outcome for older adolescents and young adults with ALL remains poor. In the past decade, next generation sequencing has enabled critical advances in our understanding of leukemogenesis. These include the identification of risk-associated ALL subtypes (e.g., those with rearrangements of MEF2D, DUX4, NUTM1, ZNF384 and BCL11B; the PAX5 P80R and IKZF1 N159Y mutations; and genomic phenocopies such as Ph-like ALL) and the genomic basis of disease evolution. These advances have been complemented by the development of novel therapeutic approaches, including those that are of mutation-specific, such as tyrosine kinase inhibitors, and those that are mutation-agnostic, including antibody and cellular immunotherapies, and protein degradation strategies such as proteolysis-targeting chimeras. Herein, we review the genetic taxonomy of ALL with a focus on clinical implications and the implementation of genomic diagnostic approaches.

Therapeutic strategies, including allogeneic stem cell transplantation, to overcome relapsed/refractory adult T-cell acute lymphoblastic leukemia

INTRODUCTION

The long-term survival of relapsed/refractory (R/R) adult T-cell acute lymphoblastic leukemia (T-ALL) is quite poor, and early T-cell precursor (ETP) ALL has recently been described as a high-risk T-ALL subgroup. However, the optimal therapeutic approach to R/R adult T-ALL remains poorly established.

AREAS COVERED

At present, cytoreductive therapy followed by allogeneic stem cell transplantation (allo-SCT) is considered to be the most clinically relevant and curative modality for R/R T-ALL. Above all, achieving minimal residual disease (MRD) is a key factor for successful allo-SCT and maintaining long-term remission for R/R patients. As a salvage regimen, nelarabine is the only therapy that was specifically approved for use in patients with R/R T-ALL. A combination of conventional chemotherapeutic agents and novel agents, such as venetoclax, can be used as alternatives for cytoreduction and bridging to transplantation. Relevant literatures published in the last 30 years were searched from PubMed to review the topic of T-ALL, and allo-SCT.

EXPERT OPINION

An effective salvage regimen, to achieve negative MRD, followed by allo-SCT is currently the best way to improve the clinical outcomes of adult R/R T-ALL. Moreover, posttransplant therapies, such as prophylactic or preemptive donor leukocyte infusion and hypomethylating agents, need to be considered as sequential therapy.