The future of omics data mining for cancer research and drug discovery

Q-omics is an innovative cancer research platform empowering scientists to explore and analyze multi-omics data independently, freeing them from reliance on external services.

Its user-friendly interface and AI-powered capabilities enable the identification of crucial cancer-related biomarkers, drug targets, and therapeutic strategies.

* Here's how it helps

Wide range of applications include:

Discovery studies in drugs, targets and biomarkers

Underlying mechanisms in omics data analysis

Gene-to-function inpterpretation

Agnostic target discovery

Tumor-specific neoantiens

Synthetic lehtal gene discovery

Immunotherapy markers

Tumor-infiltrating cells

And many more ...



Available data integrated for cross-association analysis

Data set # Lineages # Samples # Entities Data type Source/Download
Cell line data RNA expression 20 1,061 19,137 RNA sequencing CCLE[1]/Depmap Expression 20Q1[2]
Protein expression and phosphorylation 20 899 214 Reverse Phase Protein Array Depmap/Protein Array[2]
Protein abundance 20 949 8,424 Mass spectrometry [11]
sgRNA 20 741 18,110 CRISPR Depmap/CRISPR 20Q1[2]
shRNA 20 587 16,800 RNAi shRNA Depmap/RNAi (Achilles + DRIVE + Marcotte, DEMETER2)[2]
Drug response 20 1,001 561 Drug response GDSC[3]/Depmap Sanger GDSC1 and GDSC2[2]
Mutations 20 1,281 18,731 Exome sequencing CCLE/Depmap Mutation 20Q1[2]
Drug-induced RNA expression 13 60 12,305 (genes)
15 (drugs)
DNA microarray NCI60[4]/GSE116436[5]
RNA expression enriched GO function 20 1,061 7,172 (GO) Enrichment score GO[14]
Protein abundance enriched GO function 20 848 7,172 (GO) Enrichment score GO[14]
Tissue data Tumor RNA expression 34 9,951 38,311 RNA sequencing TCGA[6], GENIE[7], CPTAC[12] /GDC portal[8]
Protein expression and phosphorylation 32 6,985 275 Reverse Phase Protein Array TCGA/GDC portal
Protein abundance and post-translational modification 10 1,067 >50,000 Mass spectrometry CPTAC/PDC portal[13]
Paired normal vs. tumor: RNA expression 18 679 38,311 RNA sequencing TCGA/GDC portal
Somatic mutations 34 9,100 20,850 Exome sequencing TCGA, GENIE/GDC portal
Infiltrating cells 33 8,954 64 (cell types) Cell type enrichment score xCell[9]/xCell portal[10]
RNA expression enriched GO function 34 9,951 7,172 (GO) Enrichment score GO[14]

[1] Barretina et al., (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607.

[2] https://depmap.org/portal/download/

[3] Yang, et al., (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. 41 (Database issue): D955 - D961.

[4] Monks, et al. (2018) The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel. Cancer Res 78, 6807-6817.

[5] https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116436

[6] Cancer Genome Atlas Research, N., et al. (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet 45, 1113-1120.

[7] AACR Project GENIE Consortium, (2017) AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discovery 7(8):818-831.

[8] https://portal.gdc.cancer.gov/

[9] Dvir A., Zicheng H. and Atul J.B. (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology 18:220.

[10] https://xcell.ucsf.edu/

[11] Gonçalves, et al. (2022) Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40(8), 835-849.

[12] https://proteomics.cancer.gov/

[13] https://proteomic.datacommons.cancer.gov/pdc/

[14] https://geneontology.org/

Pre-calculated associated data pairs in Smart DBs

Database name Analysis type # Tables # Records
Cross association in cell line samples Cross association hits between pairs of entities among 8 datasets 31[1] 1,817,144,623
Cross association in patient samples Cross association hits between pairs of entities among 6 datasets 14[2] 9,542,951,055
Survival analysis in patient samples Survival hits using 6 datasets 6[3] 1,002,083
Survival analysis between omics datasets in patient samples Survival hits between omics datasets using 6 datasets 22[4] 16,421,400,938
Cross association between omics dataset and drug response in patient samples Cross association hits between 5 datasets and drug response 4[5] 915,619
Drug-induced RNA expression and GO function in cell line samples Differentially expressed RNA expression and enriched GO function hits using drug concentrations and drug treatment times 2[6] 372,905
Comparison between normal vs. tumor tissue in patient samples Differential hits using 5 datasets 5[7] 662,331
Synthetic lethal analysis in cell line and patient samples Down-regulated gene pair hits between sgRNA vs. RNA expression and shRNA vs. RNA expression (cell line samples), and down-regulated survival hits between RNA expression vs. RNA expression (patient samples) 3[8] 12,406,678
Gene, Drug, GO function summary Hit summary per gene/drug/GO function in smart DB 3[9] 964,670

Abbreviation: Protein (RPPA) for protein expression and phosphorylation by Reverse Phase Protein Array (RPPA), Protein (MS) for Protein abundance and post-translational modification by Mass spectrometry

[1] Drug response vs. Drug response, Drug response vs. Protein (RPPA), GO function enriched by Protein (MS) vs. Drug response, GO function enriched by RNA expression vs. Drug response, GO function enriched by RNA expression vs. Protein (MS), Mutation vs. Drug response, Mutation vs. Mutation, Mutation vs. RNA expression, Mutation vs. sgRNA, Mutation vs. shRNA, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA), Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. sgRNA, Protein (RPPA) vs. shRNA, Protein (MS) vs. Drug response, Protein (MS) vs. Mutation, Protein (MS) vs. Protein (RPPA), Protein (MS) vs. Protein (MS), Protein (MS) vs. RNA expression, Protein (MS) vs. sgRNA, Protein (MS) vs. shRNA, RNA expression vs. Drug response, RNA expression vs. RNA expression, sgRNA vs. Drug response, sgRNA vs. RNA expression, sgRNA vs. sgRNA, shRNA vs. Drug response, shRNA vs. RNA expression, shRNA vs. sgRNA, shRNA vs. shRNA

[2] Infiltrating cell vs. Infiltrating cell, Infiltrating cell vs. Protein (MS), RNA expression vs. Infiltrating cell, RNA expression vs. RNA expression, RNA expression vs. Mutation, GSGene vs. Infiltrating cell, Protein (MS) vs. RNA expression, Protein (MS) vs. Protein (MS), Protein (MS) vs. Mutation, Mutation vs. Infiltrating cell, Protein (RPPA) vs. Infiltrating cell, Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA)

[3] RNA expression, Mutation, Infiltrating cell, Protein, MS, GSGene

[4] Infiltrating cell vs. Infiltrating cell, Infiltrating cell vs. Mutation, Infiltrating cell vs. Protein (MS), Infiltrating cell vs. Protein (RPPA), Infiltrating cell vs. RNA expression, RNA expression vs. Infiltrating cell, RNA expression vs. Mutation, RNA expression vs. Protein (MS), RNA expression vs. Protein (RPPA), RNA expression vs. RNA expression, Protein (MS) vs. Infiltrating cell, Protein (MS) vs. RNA expression, Protein (MS) vs. Protein (MS), Protein (MS) vs. Mutation, Mutation vs. Infiltrating cell, Mutation vs. Mutation, Mutation vs. Protein (MS), Mutation vs. Protein (RPPA), Mutation vs. RNA expression, Protein (RPPA) vs. Infiltrating cell, Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA)

[6] Differentially expressed RNA expression and differentially enriched GO function

[7] RNA expression, Protein (MS), Infiltrating cell, RNA expression enriched GO function, and Protein (MS) enriched GO function

[8] sgRNA vs. RNA expression, shRNA vs. RNA expression, and RNA expression vs. RNA expression

[9] Gene, drug, and GO function