Addition of Drug-Response Specific Micro-RNAs to the International Prognostic Index Improves Prognostic Stratification of GCB-DLBCL Patients Treated with R-CHOP

Background: Patients with Diffuse large B-cell lymphoma (DLBCL) in approximately 40% of cases suffer from primary refractory disease and treatment induced immuno-chemotherapy resistance demonstrating that standard provided treatment regimens are not sufficient to cure all patients. Early detection of resistance is of great importance and defining microRNA (miRNA) involvement in resistance could be useful to guide treatment selection and help monitor treatment administration while sparing patients for inefficient, but still toxic therapy. Concept and Aims: With information on drug-response specific miRNAs, we hypothesized that multi-miRNA panels can improve robustness of individual clinical markers and serve as a prognostic classifier predicting disease progression in DLBCL patients. Methods: Fifteen DLBCL cell lines were tested for sensitivity towards rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). Cell line specific seeding concentrations was used to ensure exponential growth and each cell line was subjected to 16 concentrations in serial 2-fold dilutions and number of metabolic active cells was evaluated after 48 hours of drug exposure using MTS assay. For each drug, we ranked the cell lines according to their sensitivity and categorized them as sensitive, intermediate responsive, or resistant. Differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs to be associated with response to compounds of the R-CHOP regimen, by selecting probes with a log fold change larger than 2. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis identified 43 miRNAs associated to response to R-CHOP. Using the Affymetrix HG-U133+2 platform, expression levels of the miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers were build using multiple Cox regression or random survival forest. Results: Generated prognostic miRNA-panel classifiers were tested for predictive accuracies and were subsequently evaluated by Brier scores and time varying area under the ROC curves (tAUC). Progression-free survival (PFS) was chosen as the outcome, since it is a treatment evaluation parameter as closely as possible to the time of drug exposure and the tested miRNAs were all associated directly to drug specific response. Furthermore, overall survival (OS) was used for verification of findings. Comparison of analyses conducted for the respective cohorts (All DLBCL, ABC, and GCB patients) showed the lowest prediction errors for all models within the GCB subclass with a multivariate Cox miRNA-panel model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the miRNA-panel and international prognostic index (IPI) substantially increased prognostic performance in GCB classified patients, indicating a prognostic signal from the response-specific miRNAs independent of IPI. In conclusion: We found as proof of concept that adding gene expression data detecting drug-response specific miRNAs to the clinically established IPI improved the prognostic stratification of GCB-DLBCL patients treated with R-CHOP. Disclosures No relevant conflicts of interest to declare.

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Large-Scale Labeling and Assessment of Sex Bias in Publicly Available Expression Data

10.1101/2020.10.26.356287 ◽

2020 ◽

Author(s):

Emily Flynn ◽

Annie Chang ◽

Russ B. Altman

Keyword(s):

Gene Expression ◽

Cell Line ◽

Large Scale ◽

Drug Response ◽

Drug Exposure ◽

Adverse Drug Events ◽

Human Cancer ◽

Sampling Bias ◽

Sex Bias ◽

Expression Data

ABSTRACTWomen are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we infer sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of single sex studies, split between female-only and male-only (33.3% vs 18.4% in human and 31.0% vs 30.4% in mouse respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies respectively. Our expression-based sex labels allow us to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

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Large-scale labeling and assessment of sex bias in publicly available expression data

BMC Bioinformatics ◽

10.1186/s12859-021-04070-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Emily Flynn ◽

Annie Chang ◽

Russ B. Altman

Keyword(s):

Gene Expression ◽

Cell Line ◽

Large Scale ◽

Drug Response ◽

Drug Exposure ◽

Adverse Drug Events ◽

Human Cancer ◽

Sampling Bias ◽

Sex Bias ◽

Expression Data

Abstract Background Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Results Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2–5%). Conclusions Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

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The Philadelphia Chromosome Is Not a Stable Structure and Is Vulnerable to Further Rearrangements during Disease Progression in CML

Blood ◽

10.1182/blood.v112.11.4141.4141 ◽

2008 ◽

Vol 112 (11) ◽

pp. 4141-4141

Author(s):

Anna Virgili ◽

Diana Brazma ◽

Anastasios Chanalaris ◽

Colin Grace ◽

Elisabeth Nacheva

Keyword(s):

Disease Progression ◽

Cell Line ◽

Cell Lines ◽

Copy Number ◽

Fusion Gene ◽

Blast Crisis ◽

Philadelphia Chromosome ◽

High Copy Number ◽

Haematopoietic Stem Cell ◽

Ph Chromosome

Abstract Chronic myeloid leukaemia (CML) is a pluripotent haematopoietic stem cell disorder characterized by the expression of the BCR/ABL1 fusion gene, which commonly results from formation of the Philadelphia chromosome (Ph) after a t(9;22)(q34;q11) or related variant rearrangement. BCR/ABL1 is a constitutively activated tyrosine kinase and its amplification has been described in association with resistance to imatinib in CML patients. BAC array CGH analysis on CML patients and CML cell lines (Brazma et al., 2007) revealed unexpected genomic imbalances in form of duplications and high copy number gains affecting the region immediately downstream of the ABL1 gene at the Philadelphia (Ph) chromosome in patients at the blast crisis stage. We aimed to confirm and map these amplifications by fluorescence in situ hybridization (FISH) on 19 CML patients in accelerated phase/blast crisis and 10 CML cell lines (KU812, K562, MEG-01, MC3, BV173, EM-2, LAMA-84, KCL-22, JK-1 and CML-T1) with more than 1 copy of the BCR/ABL1 fusion gene. We used a range of BAC probes and 9q and 22q sub-telomeric probes in order to do the FISH mapping. While the majority of the analysed patients and cell lines (12/19 patients and 6/10 CML cell lines) had 2 identical Ph chromosomes, 2 main groups of abnormalities were identified. Firstly, gains of the Ph chromosome taking the form of ider(22)t(9;22) chromosome were detected in 1 or more copies in a subset of bone marrow cells of 5/19 patients and, secondly, high copy number gains were seen in 2/19 patients and 2/10 cell lines (K562 and KU812). The amplified region was variable in size spanning from 400 Kb up to 1.5 Mb downstream of the ABL1 gene. In 1 patient, 7 different cell sub-clones harbouring increasing levels of amplification were found. The gains resulted in formation of different chromosome structures-from an ider(22)t(9;22) to markers with tandem amplifications, which included the BCR/ABL1 fusion with variable in length sequences downstream of the ABL1. Duplication of some 571 Kb downstream of ABL1 was also detected in 1 of the 2 apparently normal Ph chromosomes in the MC3 cell line, while a larger duplication (5.16 Mb) was found in another cell line (MEG-01). These findings confirm the presence of duplications and high level amplifications at the der(22) t(9;22) in CML patients and that the sequences involved are variable in length, indicating that the Ph chromosome is an unstable structure and vulnerable to further rearrangements during disease progression.

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The Potential Role of STAT's in Anti-Leukemic Therapy with Different Drugs

Blood ◽

10.1182/blood.v120.21.5120.5120 ◽

2012 ◽

Vol 120 (21) ◽

pp. 5120-5120

Author(s):

Hatice Demet Kiper ◽

Burcin Tezcanli Kaymaz ◽

Ozlem Purclutepe ◽

Ceyda Tunakan Dalgic ◽

Nur Selvi ◽

...

Keyword(s):

Cell Line ◽

Cell Lines ◽

Drug Exposure ◽

Conflicts Of Interest ◽

Time Dependent ◽

Blue Dye ◽

Dependent Manner ◽

Down Regulation ◽

Expression Levels ◽

Induced Apoptosis

Abstract Abstract 5120 STAT pathways play a pivotal role in oncogenesis and leukemogenesis, thus targeting STAT signalling appears to be an effective anticancer treatment strategy. It has been described that constitutive activation of STAT3 and STAT5 plays a pro-oncogenic role both in acute and chronic myeloid neoplasms. In this study, we aimed to clarify the potential relationship between drug-induced apoptosis with different agents and STAT pathway. A third-generation bisphosphonate; zoledronate, an angiotensin-converting enzyme inhibitor (ACE-I); enalapril, a proteasome inhibitor which is used for treatment of multiple myeloma; bortezomib and a second-generation tyrosine kinase inhibitor; dasatinib were examined in this goal. Cell viability and cytotoxicity tests were conducted by using Trypan blue dye exclusion and XTT assays, respectively. Apoptotic analyses were performed by AnnexinV-EGFP staining method and fluorescence microscopy. Expression levels of STAT3, −5A and −5B genes were analysed in myeloid cell lines by qRT-PCR. The results showed that zoledronate; bortezomib and dasatinib decreased viability and proliferation and induced apoptosis in CML cell line K562 in a dose- and time-dependent manner which is associated by prominent decrease of STAT3, STAT5A and STAT5B mRNA expressions. Enalapril was also found to be cytotoxic and induced apoptosis in APL cell line HL60 in a dose- and time-dependent manner and the expression levels of STAT5A gene have significantly reduced in enalapril-treated HL60 cells as compared to untreated controls. Treatments of cell lines with other drugs were also associated with significant apoptosis in certain time points. The results and changes in expression of STAT's in mRNA level at 72nd hours are summarized in table. Taken together all these data showed that targeting STAT pathways by different drugs may be an appropriate approach in anti-leukemic therapy. This finding is important to propose that discovery or identification of novel agents targeted STATs may open new windows to the other hematological and solid malignancies which are associated with aberrant STAT expression. Table: The changes in STAT expressions after drug exposure in time-dependent manner with the dose of IC50. DRUGS CELL LINE IC50 APOPTOSIS (%) STAT3 mRNA Down Regulation (%) STAT5A mRNA Down Regulation (%) STAT5B mRNA Down Regulation (%) ENALAPRIL HL-60 7 μM 20 20* 76 5* ZOLEDRONATE K562 60 μM 34 63 31 57 BORTEZOMIB K562 177 μM 37 98 100 99 DASATINIB K562 3,314 nM 75 NA 33 78 * : Not significant NA: not applied Disclosures: No relevant conflicts of interest to declare.

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Connecting gene expression subtypes of colorectal cancer (CRC) with cell lines and drug resistance.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e14544 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e14544-e14544

Author(s):

Eva Budinska ◽

Jenny Wilding ◽

Vlad Calin Popovici ◽

Edoardo Missiaglia ◽

Arnaud Roth ◽

...

Keyword(s):

Gene Expression ◽

Cell Line ◽

Clinical Significance ◽

Cell Lines ◽

Drug Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis

e14544 Background: We identified CRC gene expression subtypes (ASCO 2012, #3511), which associate with established parameters of outcome as well as relevant biological motifs. We now substantiate their biological and potentially clinical significance by linking them with cell line data and drug sensitivity, primarily attempting to identify models for the poor prognosis subtypes Mesenchymal and CIMP-H like (characterized by EMT/stroma and immune-associated gene modules, respectively). Methods: We analyzed gene expression profiles of 35 publicly available cell lines with sensitivity data for 82 drug compounds, and our 94 cell lines with data on sensitivity for 7 compounds and colony morphology. As in vitro, stromal and immune-associated genes loose their relevance, we trained a new classifier based on genes expressed in both systems, which identifies the subtypes in both tissue and cell cultures. Cell line subtypes were validated by comparing their enrichment for molecular markers with that of our CRC subtypes. Drug sensitivity was assessed by linking original subtypes with 92 drug response signatures (MsigDB) via gene set enrichment analysis, and by screening drug sensitivity of cell line panels against our subtypes (Kruskal-Wallis test). Results: Of the cell lines 70% could be assigned to a subtype with a probability as high as 0.95. The cell line subtypes were significantly associated with their KRAS, BRAF and MSI status and corresponded to our CRC subtypes. Interestingly, the cell lines which in matrigel created a network of undifferentiated cells were assigned to the Mesenchymal subtype. Drug response studies revealed potential sensitivity of subtypes to multiple compounds, in addition to what could be predicted based on their mutational profile (e.g. sensitivity of the CIMP-H subtype to Dasatinib, p<0.01). Conclusions: Our data support the biological and potentially clinical significance of the CRC subtypes in their association with cell line models, including results of drug sensitivity analysis. Our subtypes might not only have prognostic value but might also be predictive for response to drugs. Subtyping cell lines further substantiates their significance as relevant model for functional studies.

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Regulatory landscapes of specific miRNAs are conserved between cell lines and primary tumors

F1000Research ◽

10.12688/f1000research.52478.1 ◽

2021 ◽

Vol 10 ◽

pp. 633

Author(s):

Hanwen Zhu ◽

Boting Ning

Keyword(s):

Gene Expression ◽

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Mirna Expression ◽

Regulatory Network ◽

Human Cancer ◽

Cancer Cell Line ◽

Cancer Type ◽

Cancer Tissues

Background: MicroRNAs are essential gene expression regulators and play important roles in various biological processes, such as cancer. They have shown great translational promise as either diagnostic biomarkers or therapeutic targets. While the similarities between transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia have been thoroughly studied before, less is known on the microRNA side. This project aims to provide critical biological knowledge on the extent of consensus microRNA expression and regulation between cell line models and primary human tumors. Method: First, we examined the similarity of miRNA expression profiles between CCLE cell lines and TCGA tumor samples for each cancer type. Next, we compared the expression of miRNAs associating the hallmarks of cancer pathways. Finally, we constructed miRNA-mRNA regulatory network for each cancer type and evaluated whether the regulatory role of each miRNA is conserved between cell lines and tumor samples. Results: Our results indicate that, similar to gene expression, how well cancer cell line microRNA expression would capture the transcriptomic profile of human cancer tissues is greatly affected by the tumor type and purity. The cell-type composition for a cancer type also affects how accurately cancer cell lines could reflect the miRNA expression in tumor tissues. Furthermore, through network analysis, we show that certain microRNAs, not all, regulate the same set of target genes in both the cell line and human cancer tissues. Conclusions: Through systematically comparing the miRNA expression profile and the regulatory network, our study highlights the biological differences between cell line and tumor samples and provides resources for future miRNA and cancer studies.

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Improving Drug Response Prediction Using Dual Similarity Regularization

10.21203/rs.3.rs-50015/v2 ◽

2021 ◽

Author(s):

Ali Reza Ebadi ◽

Ali Soleimani ◽

Abdulbaghi Ghaderzadeh

Keyword(s):

Cell Line ◽

Cell Lines ◽

Matrix Factorization ◽

Drug Response ◽

Pearson Correlation ◽

Heterogeneous Data ◽

Latent Factors ◽

Proposed Model ◽

Similar Cell ◽

Drug Similarity

Abstract Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors. We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug. Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.

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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

F1000Research ◽

10.12688/f1000research.10529.1 ◽

2016 ◽

Vol 5 ◽

pp. 2927 ◽

Cited By ~ 9

Author(s):

Linh Nguyen ◽

Cuong C Dang ◽

Pedro J. Ballester

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Cell Line ◽

Cell Lines ◽

Gene Expression Data ◽

Single Gene ◽

Cancer Cell Line ◽

Expression Data ◽

Gene Markers ◽

Pan Cancer

Background:Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.Methods:Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation.Results and Discussion:Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.Conclusions:We now know that this type of models can predictin vitrotumour response to these drugs. These models can thus be further investigated onin vivotumour models.

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Associations between Microrna Expression, Disease Progression and Ex Vivo Drug Response in Multiple Myeloma

Blood ◽

10.1182/blood-2019-128181 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3069-3069

Author(s):

Romika Kumari ◽

Ashwini Kumar ◽

Alun Parsons ◽

Minna Suvela ◽

Juha Lievonen ◽

...

Keyword(s):

Multiple Myeloma ◽

Disease Progression ◽

Mirna Expression ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Mtor Inhibitors ◽

P Value ◽

Diagnosis And Prognosis

Targeted drug treatment strategies have significantly prolonged the overall survival rate among multiple myeloma (MM) patients. However, high relapse rates and multiple drug resistance still pose major challenges. Although, the underlying molecular features of the disease have been explored both at the genomic and transcriptomic levels, the functional role of microRNAs (miRNA) in MM disease progression and prognosis is yet to be investigated at a personalized level. In earlier studies, microRNAs have been implicated to regulate gene expression and were determined to play crucial roles in the biology of MM by acting as oncogenes or tumor suppressors. Nevertheless, considering the heterogeneity of MM, little is known about the roles of miRNAs in controlling MM disease progression and drug response at an individualized systems level. We collected bone marrow aspirates from MM patients at diagnosis (n=20) and relapse (n=25) after informed consent and following approved protocols in accordance with the Declaration of Helsinki. CD138+ plasma cells were enriched from the bone marrow samples and used for miRNA-sequencing and drug sensitivity and resistance testing (DSRT). The miRNA was prepared from the CD138+ cells and subjected to sequencing using Illumina compatible technologies. DSRT was performed and responses to 83 clinically approved drugs and investigational compounds were measured as drug sensitivity scores (DSS) as described previously (Majumder et al., Oncotarget 2017). The pairwise comparative analysis of miRNA expression and drug responses was performed using Spearman's rank-order correlations, to elucidate significant associations of miRNA expression with drug sensitivity and resistance. Additionally, using DEseq2 the differential miRNA expression was determined for the newly diagnosed and relapse samples to deconvolute the role of miRNAs in MM disease progression. The comparative analysis of the miRNA expression and drug sensitivity scores revealed statistically significant associations between miRNA expression and drug sensitivity measures with the Spearman coefficient (r) ranging from -0.71 to 0.64 (adjusted p-value ≤ 0.05) (Figure 1A). Negative associations were more prevalent, with 40 miRNAs negatively associated with ≥1 drug response from the total of 30 predicted drugs. miR-486, which is known to be an effective biomarker in diagnosis and prognosis of multiple cancer types (Jiang et al., Oncotarget 2018), was found to have significant negative correlation (r= -0.71 to -0.52, p-value ≤ 0.01) with the responses of 14 drugs. Similarly, negative correlation was observed for miR-144 with 12 drugs and miR-584 with 9 drugs. We observed that PI3K/mTOR inhibitors and HDAC inhibitors were common amongst all the significant negative correlations predicted. Specifically, the PI3K/mTOR inhibitors apitosilib, omipalisib and buparlisib were found to be negatively associated with the expression of 18, 14 and 7 miRNAs respectively. These observations can lead to the understanding of miRNA mediated regulation of molecular pathways involved in drug resistance. Differential miRNA expression analysis between newly diagnosed and relapse MM samples revealed the involvement of miRNAs in disease progression. The analysis resulted in total of 31 significant differentially expressed miRNAs with fold change ≥2 and adjusted p-value ≤ 0.1 (Figure 1B). Several miRNAs known to play crucial roles in cancer diagnosis and prognosis were found to be significantly upregulated in the relapse samples. In particular, 25 miRNAs were upregulated, including following miR-17/92 cluster members: miR-18b, miR-20a, miR-92b and miR-106a, which are known to have an oncogenic role in various cancer types (Mogilyansky & Rigoutsos, Cell Death and Differentiation 2013). Interestingly, 12/31 differentially regulated miRNAs were located on chromosome X. Although cytogenetic alteration data predicted that chromosome 1q gain is significantly prominent in the relapse samples (p-value = 0.009), only 3/31 differentially regulated miRNAs were located on chromosome 1. These results demonstrate the role of miRNAs in regulating drug response and disease progression in multiple myeloma. Monitoring miRNA expression profiles in MM patients can facilitate the assessment of treatment outcome and prognosis, and miRNAs could potentially be useful prognostic and treatment biomarkers for MM. Disclosures Silvennoinen: Amgen: Research Funding; Bristol-Myers Squibb (BMS): Research Funding; Takeda: Research Funding; Celgene: Research Funding. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

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A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity

10.1101/2021.12.03.471100 ◽

2021 ◽

Author(s):

Sara Pidò ◽

Carolina Testa ◽

Pietro Pinoli

Keyword(s):

Cell Line ◽

Cell Lines ◽

Drug Response ◽

Missing Values ◽

Drug Sensitivity ◽

Factorization Method ◽

Data Types ◽

Drug Profile ◽

The Matrix ◽

Ic50 Values

AbstractLarge annotated cell line collections have been proven to enable the prediction of drug response in the preclinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from CCLE, containing the connections among cell lines and drugs by means of their IC50 values. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.

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