TENM2 gene) is expressed together with its host gene. TENM2 is characterized by hypomethylation and open chromatin around its TSS in cHL cell lines in contrast to NHL cell lines and germinal centre B-cells respectively. It indicates an epigenetic mechanism responsible for aberrant expression of both, the TENM2 gene and the novel miRNA in cHL cell lines. Despite the GO analysis performed with the input of the in silico predicted novel miRNA target genes did not reveal ontologies typically associated with cHL pathogenesis, it pointed to several interesting candidates involved in i.e. lymphopoiesis. These include the lymphoma related BCL11A gene, the IKZF2 gene involved in lymphocyte development or the transcription initiator GTF2H1.MicroRNAs (miRNAs) are small non coding RNAs responsible for posttranscriptional regulation of gene expression. Even though almost 2000 precursors have been described so far, additional miRNAs are still being discovered in normal as well as malignant cells. Alike protein coding genes, miRNAs may acquire oncogenic properties in consequence of altered expression or presence of gain or loss of function mutations. In this study we mined datasets from miRNA expression profiling (miRNA-seq) of 7 classic Hodgkin Lymphoma (cHL) cell lines, 10 non-Hodgkin lymphoma (NHL) cell lines and 56 samples of germinal center derived B-cell lymphomas. Our aim was to discover potential novel cHL oncomiRs not reported in miRBase (release 22.1) and expressed in cHL cell lines but no other B-cell lymphomas. We identified six such miRNA candidates in cHL cell lines and verified the expression of two of them encoded at chr2:212678788–212678849 and chr5:168090507–168090561 (GRCh38). Interestingly, we showed that one of the validated miRNAs (located in an intron of the  MicroRNAs (miRNAs) play a key role in posttranscriptional regulation of gene expression, and are essential regulators of various cellular processes ,2. TheseIn our recent study we have performed high throughput screening of miRNA expression in 7 widely used cHL cell lines and ten non-Hodgkin lymphoma cell lines (NHL) by RNA-seq . Here we7 cHL cell lines  B cells were purified from remnants of fresh tonsils of routine tonsillectomy samples using magnetic activated cell sorting , as reported previously [Normal CD77eviously . Primaryhttp://bioinformatics.mayo.edu/research/cap-mirseq/). miRNA candidates were compared to known, annotated miRNAs from miRBase (release 22.1) [The BAM files from miRNA-seq of seven cHL cell lines and 10 NHL cell lines  were rease 22.1)  and seleTo identify potential miRNAs expressed in cHL we used the following criteria:Not annotated miRNA expressed in at least 3/7 of the analyzed cHL cell lines and absent in NHL cell lines (“expressed” defined as mean ≥ 20 CPM (Counts Per Million) in cell lines with expression).Not annotated miRNAs expressed in less than 2% of the analyzed samples from previously published miRnome sequencing data including 56 primary germinal center derived B-cell lymphomas  Manual curation in order to exclude known miRNAs, palindromic sequences, artefacts and other small ncRNAs.TM Advanced miRNA cDNA Synthesis Kit (Thermo Fisher Scientific) [S1 Table). Relative expression of selected miRNAs was calculated using BioRad Genex software [cDNA templates for RT-qPCR we obtained by reverse transcription of 10 ng of total RNA  and 10 μl of miRNA containing eluate  using Tentific)  accordinsoftware  in relatsoftware .S2 Table). PCR reactions were performed as described previously [ACTB and GAPDH reference genes. The BioRad Genex software determines particular cycles (Ct) using automatically generated background values. Next, for each sample, it calculates the quantity relative to control genes, taking into account the amplification efficiency of qPCR for each gene [For mRNA expression analysis total RNA (500 ng) isolated from the cell lines using Trizol  was reveeviously . The resach gene .S3 Table). PCR products were cloned into the pCDH-CMV-MCS-EF1α-Green Puro cloning vector  and used for transformation of JM109 E.coli competent cells (Promega) as described previously [TM BAC DNA kit . For transfection of the HEK-293 cell line the Lipofectamine RNAiMAX Transfection Reagent  was used according to the manufacturer’s protocol. For control purposes the same cell line was transfected using the empty vector. Each transfection was performed in two biological replicates. Two days past transfection, total RNA was isolated using Trizol. Reverse transcription and real-time qPCR reactions were performed as described above. The transfection efficiency of HEK-293 cells was measured using JuLi Br & FL Station  by the calculation of GFP positive cells. RT-qPCR miRNA expression from the construct was measured using same TaqManTM probes as described above in reference to hsa-miR-423-3p  [Selected novel miRNA genes were amplified with 100 bp flanks using primers designed with the Primer 3 Plus software  followed by ethanol precipitation and used for bisulfite conversion.S4 Table) designed using the PyroMark Assay Design 2.0 software  were used for DNA methylation analysis of three regions corresponding to the two novel miRNA candidates:DNA was bisulfite converted using the EpiTect DNA Modification Kit  according to the manufacturer’s protocol. PCR reactions were prepared using PyroMark PCR Kit (Qiagen) in a DNA/RNA UV-cleaner box UVC/T-AR . The PCR reaction mixture contained: 12.5 μl PyroMark Master Mix; 0.5 μl (20 pmol/μl) of F and R primer, 2.4 μl CoralLoad, 1 μl of converted DNA (~25 ng/μl) and 8 μl of RNase-Free water. PCR primers (assays) (region (I) located 54 bp upstream of the miRNA gene, amplified sequence: chr2:212,678,797–212,679,018 (GRCh38/hg38), genomic position of analysed dinucleotides: chr2:212,678,922; chr2:212,678,938; chr2:212,678,945 (GRCh38/hg38).2_nv_chr2_212678788 novel miRNA candidate:TENM2 , the genomic position of analysed dinucleotide: chr5:167,284,682 (GRCh38/hg38),Region (I) 166 bp upstream of Region (II); ~2 kb upstream of the miRNA gene, amplified sequence: chr5:168,088,403–168,088,609 (GRCh38/hg38), genomic position of analysed dinucleotides: chr5:168,088,484; chr5:168,088,488; chr5:168,088,497; chr5:168,088,504; chr5:168,088,522; chr5:168,088,531; chr5:168,088,535 (GRCh38/hg38).3_nv_chr5_168090507 novel miRNA candidate:® Whole Genome Amplification Kit .PCR reactions were performed in the following conditions: 95°C for 15 min × 1; 94°C for 30 s, 59°C for 30 s, 72°C for 30 s × 45; 72°C for 10 min × 1; 4°C ∞. The products were visualised on 1.8% agarose gel with SimplySafe™ (EURx) under UV light . Purification of PCR products and pyrosequencing was performed as described previously . Each py6 cells per cell line, cHL and GCB respectively) was performed by incubating the cells in 1% formaldehyde for 8 minutes in RT. Thereafter, genomic DNA was sonicated using Covaris™ E220 device in order to obtain 50–500 bp length DNA fragments. Chromatin preparation and histone ChIP were performed according to the BLUEPRINT protocols (https://www.blueprint-epigenome.eu/index.cfm?p=7BF8A4B6-F4FE-861A-2AD57A08D63D0B58) using the anti-H3K27ac  antibody. The library for high-throughput sequencing was prepared using Kapa Hyper Prep Kit  according to the BLUEPRINT protocol. Briefly, end repair and A-Tailing was followed by adapter ligation. Library amplification was performed in the following conditions 98°C for 45 s x1; 98°C for 15 s, 60°C for 30 s, 72°C for 30 s x 12; 72°C for 1 min; 12°C ∞. PCR products were cleaned using AMPure XP beads followed by size selection of DNA fragments (300 bp) with E-Gel™ SizeSelect™ .Cross-linking of cHL cells (7 cHL cell lines) and 3 germinal center B-cell pools (GCBs), (~10*10High-throughput sequencing was carried out using the HiSeq2500 (Illumina) sequencer . Raw reaIn order to identify potential gene targets for the novel experimentally confirmed miRNAs two freely available tools were used: miRDB  and miRatotal score > 280 for miRanda and > 80 for miRDB,only genes present in both cohorts for each of the analysed novel miRNA respectively.These gene sets were used as the input for Gene Ontology (GO) analyses performed using STRING  and GO CS1 Table). The RT-qPCR products were visualized on 1.8% agarose gel with SimplySafe™ (EURx) under UV light  and purified using DNA Clean & Concentrator–5 Kit . PCR products were subjected to A-tailing procedure  followed by ligation reaction with pGEM ®-T Easy Vector . Obtained constructs were used for transformation of JM109 E.coli competent cells (Promega). Lastly, colony PCR was performed using M13 primers (S5 Table) according to the following protocol: 95°C for 2 min × 1;  × 30, 72°C for 5 min × 1. Obtained PCR products were then visualised, purified and sequenced (Sanger) as described previously [For additional validation of novel miRNA candidates, cDNA templates and RT-qPCR products were obtained using Custom TaqMan™ Small RNA Assay (Thermo Fisher Scientific) with the same custom miRNA probes as descripted above . Two out of the six novel miRNA candidates were confirmed to be expressed in the cHL cell lines (Table 1). The novel miRNA (2_nv_chr2_212678788) was confirmed to be expressed by RT-qPCR in 4/7 cHL cell lines  and absent in the remaining cHL and NHL cell lines in agreement with the RNA-seq data .All six selected miRNAs were tested using TaqManFig 2B). In summary, these analyses allowed the identification of two novel miRNAs recurrently expressed in cHL cell lines.The second novel molecule (3_nv_chr5_168090507) was confirmed to be expressed by RT-qPCR in 2 of 3 cHL cell lines  indicated initially by the NGS experiment . We used an alternative reverse transcription kit which allows individual amplification of the miRNAs of interest followed by qPCR with the same custom probes as used previously.S6 Table).Importantly, we observed expression of particular unannotated miRNAs in the same cHL cell lines as indicated by both NGS and previous RT-qPCR experiments . Lastly, we cloned the obtained PCR products and performed Sanger sequencing. It shoved the presence of particular unannotated miRNA sequences (mature miRNA molecules of nv_chr2_212678788 and 3_nv_chr5_168090507) in the respective cHL cell lines . In summary, these results demonstrate that 2_nv_chr2_212678788 and 3_nv_chr5_168090507 precursors are processed by the cell into mature miRNAs identified using the TaqManTM assays.To analyze if 2_nv_chr2_212678788 and 3_nv_chr5_168090507 undergo canonical miRNA biogenesis we cloned the PCR products containing the entire miRNA precursor sequences into the pCDH-CMV-MCS-EF1α-Green Puro cloning vector and used these constructs to transfect the HEK-293 cell line. We recurrently observed the expression of both novel miRNAs after transfection with the miRNA constructs, whereas no expression of the novel miRNAs was observed in samples transfected with the empty vector (TENM2 transcript (uc010jjd.3 –largest gene transcript with the highest level of review) as a mirtron [TENM2 intronic sequence) and the second region 166 bp upstream of the TENM2 gene TSS . The second analyzed miRNA (2_nv_chr2_212678788) is located in a gene desert, in a THE1C LTR, therefore only the region within the LTR and adjacent to the miRNA was tested (54 bp upstream).As HRS cells are characterised by global epigenetic reprogramming which includes alteration of DNA methylation of promoter regions and loss of epigenetic control of LTRs. We hypothesized that the expression of the two novel miRNAs 3_nv_chr5_168090507 and 2_nv_chr2_212678788 is triggered by specific DNA hypomethylation in the analyzed cell lines. In order to test this hypothesis three pyrosequencing assays were designed to analyze for potential hypomethylation in the respective regions. DNA methylation levels in cHL cell lines were compared to NHL cell lines that do not express these novel miRNAs. Because of the intronic localization of the 3_nv_chr5_168090507 miRNA gene, it can be transcribed canonically, as a separate miRNA gene, or non-canonically together with the large  mirtron . TherefoTENM2 promoter region (host gene of 3_nv_chr5_168090507 miRNA) using assay I; 1 CG dinucleotide  in cHL cell lines  compared to NHL cell lines . Summarizing, DNA hypomethylation of the TENM2 promoter region is a potential mechanism responsible for 3_nv_chr5_168090507 transcriptional activation in cHL.The mean methylation level in the vicinity of the 3_nv_chr5_168090507 miRNA gene  was 79%  in the cHL cell lines and 82%  in the NHL cell lines showing no significant difference in DNA methylation level between the studied lymphoma types. Similarly, no differences were observed for the LTR located 2_nv_chr2_212678788 miRNA for which the mean methylation level (3 CG dinucleotides) was 76%  in the cHL cell lines and 75%  in the NHL cell lines. This finding suggests that activation 2_nv_chr2_212678788 miRNA is not a direct effect of the respective LTR hypomethylation. However, we observed significant hypomethylation (p<0.05) of the TENM2 promoter region indeed results in elevated expression of the gene (3_nv_chr5_168090507 primary transcript) we performed RT-qPCR to quantify TENM2 expression. It indicated a negative correlation   between the level of expression and DNA methylation of TENM2 promoter region. These data suggest that TENM2 is epigenetically regulated in cHL and NHL cell lines. The expression of miRNA 3_nv_chr5_168090507 in the KM-H2, SUP-HD-1, and HDLM-2 cell lines, which also showed the highest TENM2 expression, renders it likely that this novel miRNA is transcribed together with the TENM2 host-gene.Moreover, to test if hypomethylation observed in the TENM2 promoter region to look for changes in the chromatin activity. The experiment showed open chromatin regions near proximal TENM2 TSS the cHL cell lines compared to fully closed chromatin in the GCB cell controls  and the studied NHL cell lines: BL2, DG-75, KARPAS-422, SU-DHL-5, Z-138 . The highest chromatin activity was observed in KM-H2, SUP-HD-1 and HDLM-2 cell lines  what translates into elevated relative expression of TENM2 and the novel miRNA in these cHL cell lines .In order to further confirm this mechanism we studied ChIP-seq data (H3K27ac) of the 7 cHL cell lines, 3 GCB cell pools (controls) and 5 NHL cell lines . We focused on the S2 Fig), what in part may be caused by the low performance of the TaqMan probe in the microdissected samples. Therefore, the novel miRNA 3_nv_chr5_168090507 is expressed only in the analysed cHL cell lines or its expression level in the microdissected HRS cells is beyond the detection level of the used technique.In order to test if overexpression of the novel 3_nv_chr5_168090507 miRNA is only due to the deregulation of cHL cell lines or is also found in primary cHL cases, we next analysed its expression in 10 primary microdissected HRS cell pools. No expression of the novel miRNA 3_nv_chr5_168090507 was observed in the HRS cells  that triggers the tempting speculation that its aberrant expression may be driven by the LTR’s transcriptional re-activation [It has recently shown, that aberrant activation of more than 1800 LTRs in cHL participates in global transcriptional deregulation of protein coding genes in this lymphoma . MoreoveRS cells . In lightivation . We thertivation . This fihttp://mirdb.org/), miRanda (http://cbio.mskcc.org/miRNA2003/miranda.html)) for 2_nv_chr2_212678788 and 9 genes for 3_nv_chr5_168090507 indicated by both tools. The GO analysis performed using obtained cohorts of genes, resulted in 13 biological processes for 2_nv_chr2_212678788  (S7 Table) and no biological process for 3_nv_chr5_168090507. For 2_nv_chr2_212678788 these included several processes responsible for regulation of gene expression, metabolic processes and circadian regulation. Although, these processes are generally related to malignant transformation, no ontologies typically associated with cHL pathogenesis were identified.Based on the criteria outlaid in the methods section, custom miRNA target prediction for the novel miRNAs resulted in 45 genes . It may potentially explain why only two of the six novel miRNAs were confirmed experimentally. Alternatively, despite of accurate filtering of false positive results, these four unconfirmed miRNAs may be in fact low expressed artifacts wrongly annotated as miRNAs.We and other groups have recently performed high throughput experiments to characterize the miRnome of cHL cell lines ,51,52. ITENM2 gene (region <200 bp upstream of TSS) harboring the 3_nv_chr5_168090507 miRNA, in cHL cell lines compared to NHL cell lines. Moreover, we noticed the expression of TENM2 (uc010jjd.3 transcript) exclusively in the KM-H2, SUP-HD-1 and HDLM-2 cell lines that correlates to elevated chromatin activity (H3K27ac ChIP-seq) around the TENM2 TSS in these cell lines. Thus, our results indicate that DNA hypomethylation is the mechanism responsible for transcriptional activation of both, the TENM2 gene itself and the 3_nv_chr5_168090507 miRNA in cHL cell lines. Noteworthy, the TENM2 gene was not reported to be associated with cHL pathogenesis itself.By cloning and transfecting the precursor sequences of the two novel miRNAs (2_nv_chr2_212678788 and 3_nv_chr5_168090507) to the HEK-293 cell line and downstream detection of the mature sequences we demonstrated that both miRNAs are biologically processed by the cell. Therefore, these two yet unannotated miRNAs are potentially functional. However, their low expression level leaves doubts on their physiological relevance in cHL. In addition, the qRT-PCR experiments on microdissected HRS cells did not confirm the presence of these miRNAs in the primary cells. This may be on one hand due to technical reasons investigating a small number of the analyzed, pooled, microdissected cells (1000) which in combination with the low expression of these miRNAs may be below the detection level of the technique. On the other hand, this may also indicate that their low expression is a side effect of the global transcriptional reprogramming of the studied cHL cell lines or it may be indicative for therapy-refractory disease. In line with this assumption we observed DNA hypomethylation of In contrast to 3_nv_chr5_168090507 miRNA we did not identify the mechanism behind the activation of miRNA 2_nv_chr2_212678788.in silico GO analysis using the 45 (2_nv_chr2_212678788) and 9 (3_nv_chr5_168090507) target genes of these miRNAs as input. The analysis indicated an enrichment by terms related to metabolism and regulation of gene expression. How far these processes reflect malignant transformation in cHL remains to be studied.Finally, we performed In this study we discovered two unannotated miRNAs expressed exclusively in cHL cell lines. Using a functional approach, we proved that the molecules undergo a typical processing for miRNAs resulting in the expression of mature miRNA sequences. Moreover, we identified DNA hypomethylation and chromatin activity as mechanism for the transcriptional activation of 3_nv_chr5_168090507 miRNA in cHL cell lines.S1 Fighttps://www.blueprint-epigenome.eu/index.cfm?p=3D6C68FA-3048-9110-625D850E3E055A84) regarding chromatin activity of TENM2 promoter region . The ChIP-seq (H3K27ac) experiment was performed on 5 NHL cell lines. The horizontal bars near particular cell line name represent chromatin state. Black colour of the bar indicates inactive heterochromatin region, whereas other colours are showing active states of chromatin. The 27th lysine acetylation for TENM2 promoter region shows fully closed chromatin regions in all NHL cell lines.The visualisation (UCSC) of data from BLUEPRINT project ((TIF)Click here for additional data file.S2 FigExpression of 3_nv_chr5_168090507 novel miRNA in 10 primary microdissected HRS cell pools with corresponding membrane negative controls. (cHL cell lines (green) and GC B cells (red) are shown as positive and negative controls, respectively.(TIF)Click here for additional data file.S1 TableTM probes used for validation of novel miRNA candidates and sequences of two known miRNAs used as the reference.Sequences of custom TaqMan(DOCX)Click here for additional data file.S2 TablePrimer sequences used for TENM2 mRNA expression analysis.(DOCX)Click here for additional data file.S3 TableGenomic locations of amplified regions (GRCh38) and primer sequences used for amplification of two novel miRNA genes. The amplicons were used in further functional analysis. Detailed information about the amplified regions: whole amplified sequence, miRNA precursor sequence (green), mature miRNA sequence (highlighted in yellow) is presented below the table.(DOCX)Click here for additional data file.S4 TableGenomic locations of amplified regions (GRCh38) and primer sequences used for bisulfite DNA pyrosequencing  of three regions corresponding to the two novel miRNA candidates.(DOCX)Click here for additional data file.S5 TablepGEM ®-T Easy Vector primer sequences used for amplification and Sanger sequencing of nv_chr2_212678788 and 3_nv_chr5_168090507 novel miRNA sequences.(DOCX)Click here for additional data file.S6 TableSequencing of colony PCR products (pGEM-T Easy Vector with cloned novel miRNA insert obtained by qPCR). The qPCR reaction was performed using Custom TaqMan™ Small RNA assays. Last column shows the sequenicng chromatograms with respective novel miRNA sequence highlighted in blue.(XLSX)Click here for additional data file.S7 TableFunctional enrichments found in the group of 45 genes that are the putative targets of the novel 2_nv_chr2_212678788 miRNA expressed in cHL cell lines .(DOCX)Click here for additional data file.


WUSCHEL, FASCIATED, LOCULE NUMBER). Fruit shape also relies on controlled cell division and expansion patterns regulated by, for example, OVATE FAMILY PROTEIN 20 or SUN1 , a group of 5 plant-specific, microtubule-related proteins that localize to microtubules; SlMAP70-1 was chosen for further analysis since it has a higher expression during fruit development. The overexpression of SlMAP70-1 generated elongated fruits, while the knock-down lines generated short and flat ones , while the cells were more circular in the knock-down lines. Although the lobe number of the cells was not affected, the lobe length was shorter in the knock-down or knock-out lines and longer in the overexpression lines.The authors studied the MAP70 proteins in tomato  to track the microtubule organization and dynamics. Anisotropy values (reflecting order in the microtubule filaments) in endocarp cells were similar from −3 to 0 days after pollination; after that, a dramatic re-orientation of the microtubules occurred. However, in the SlIQD1 generated extremely elongated fruits; the effect of SlIQD21a was similar to SlMAP70-1, suggesting a cooperative function. The combined overexpression of SlMAP70-1 and SlIQD21a further enhanced fruit elongation. Analysis of cell features in these additional lines indicated that anisotropy in microtubules increased in the mutant line slmap70-1/2 and was reduced in the SlMAP70-1 overexpression line. Long fruits (overexpression of SlMAP70-1 or SlIQD21a) correlated with reduced cell circularity. In Arabidopsis pavement cells, high circularity correlates with low microtubule anisotropy, and cells expand isotropically. Interestingly, the opposite was observed in tomato, where increased microtubule anisotropy in endocarp cells was correlated with high circularity.To identify other microtubule-related proteins controlling fruit shape, a yeast 2-hybrid assay was performed using SlMAP70-1 as bait; SlIQD21a (SUN10) was identified as a putative interaction partner. The IQD family has 33 members in tomato and several were selected to generate overexpression lines. The overexpression of map701/2 double mutant displayed no preference for microtubule direction with respect to the growth direction, but lines overexpressing SlMAP70-1, SlIQD21a, or both showed an enrichment in transverse microtubules. Microtubules were more ordered in the slmap70-1/2 at the single cell level, but the average orientation varies from cell to cell so that cells can expand in different directions; on the other hand, in the overexpression lines, microtubules were oriented transversely, restricting lateral expansion and favoring cell elongation, resulting in elongated fruits.Microtubule dynamics were also analyzed at the tissue level to document the correlation between microtubule patterns, cell shape, and fruit shape. Endocarp cells in the SlIQD21a, SlMAP70-1, or both induced the formation of thick microtubule whorls and bundles, while this phenomenon was reduced in the double-mutant slmap70-1/2. Microtubule dynamics were also affected; in the wild-type, microtubule polymerization and depolymerization occurred at similar rates; however, in plants overexpressing SlMAP70-1 or SlIQD21a, microtubules were more static. The function of SlMAP70-1 and SlIQD21a as microtubule stabilizers was measured after chemical treatments. High concentrations of oryzalin disrupted the microtubules after 10 min; however, the increased expression of SlMAP70-1 and SlIQD21a induced a protective effect, and intact microtubules were detected 40 min after treatment.With respect to microtubule organization, the overexpression of In summary, the SlMAP70 proteins and SlIQD21a contribute to microtubule stability, affecting microtubule rearrangements and cell growth patterns. This work highlights the importance of microtubule organization and dynamics in defining organ shape.


Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction. Combination of drugs within cancer treatment is a popular way to overcome resistance and increase efficacy. Here, the authors analyse over 5000 targeted agent combinations in non-small cell lung cancer to identify potentially effective drug strategies. Drug combinations are also frequently part of anti-cancer treatment, based mainly on empirical clinical discovery for decades5. Rationally designed targeted agents have now been approved across a variety of cancers but the vast majority of patients are still treated first with combinations of “classic” genotoxic chemotherapeutic agents such as DNA damaging agents or other agents targeting cycling cells (taxanes). Targeted agents are sometimes combined with traditional cytotoxics: e.g., the targeted agent trastuzumab (an antibody against HER2) is combined with taxane to achieve higher benefit in HER2 breast cancer7. Currently, there are only few combinations involving exclusively rationally designed targeted agents that are used to treat cancer. There are however notable examples of recent successes: combining CDK4/6 inhibition with Estrogen Receptor (ER) directed therapy is beneficial over other therapies in ER positive breast cancer8. In AML, the BCL2 targeting agent venetoclax combined with the demethylating agent aza-cytidine provides substantial improvement in clinical outcome compared to either agent alone or chemotherapeutics regimens9. The use of BRAF and MEK1/2 inhibitors in combination has led to improved response in melanoma10. Many other targeted combinations are now being tested in clinical trials.Modern therapeutic approaches to numerous pathologies include the use of drug combinations to obtain better efficacy and lower systemic toxicity in patients. Combinations of drugs have been frequently used to treat microorganisms infections11. Whether combinations of targeted agents will show such heterogeneity in response or allow for more encompassing treatment regimen is not known. Another critical aspect, even for targeted agents, is toxicity. In contrast to drug combinations against HIV for example, targeted drugs against cancer address cellular processes that are almost always shared between cancer cells and normal cells. Consequently, even with targeted agents of good specificity, increased toxicity is a major hurdle for clinical development of combinations and is additionally very difficult to predict. To obtain higher efficacy than single agents and minimize systemic toxicity, drug combinations that are synergistic specifically in cancer cells are thus conceptually attractive even if synergy is not required to obtain effective combinatorial effect on cohorts of patients12. Yet, the availability of public large scale combination datasets is limited, additionally impairing efficient computational modeling for combination discovery13.While it stands to reason that combining targeted drugs could improve benefit, the rational development of drug combinations against cancer is still hampered by the limited understanding of underlying cellular processes. There is now ample evidence of heterogeneous response to targeted anti-cancer therapies even within molecularly stratified patients. Indeed, response is still highly variable within the best responsive patient cohorts, with treatment either inefficient up front (innate resistance) or of limited and unpredictable duration (acquired resistance)In this study we aimed to identify combinations of interest that could help treat non-small cell lung cancer (NSCLC) patients. Through a very large dataset we generated, together with an additional validation screen, we provide a robust estimate of the heterogeneity of response to targeted drug combinations within lung cancers and analyze genetic as well as cellular network determinants of combinatorial effect. This dataset will additionally provide a common grounds resource for the scientific community interested in drug combinations development against cancer, and in the development of computational modeling approaches towards the systematic discovery of synergism in cancer cells.14 was assembled. These models are extensively characterized at the molecular level15. Mutational profiles for major cancer genes in this collection are shown in Supplementary Fig. 16 only a handful of cancer genes17 are recurrently mutated across the cell line collection .To systematically study the response of NSCLC models to pairwise drug combinations, a collection of 81 NSCLC cell lines that are genetically representative of human tumorsion Fig. . Recentl15. In addition, 49 of the cell lines were also part of a large chemical screening effort performed across NSCLC lines surveying an initial set of >200 K compounds and an activity based selected subset of 447 chemical entities19. These single-agent datasets as well as the results of genetic perturbations using shRNA20, super potent siRNA pools21 or more recently CRISPR CAS9 mediated loss of function24 demonstrated that these NSCLC models capture the clinically relevant aspects of therapeutic response of the disease. Importantly, as observed in the clinic, these data also demonstrate a prevalent heterogeneity of response to a given perturbation even within subsets of models sharing a common oncogenic driver (heterogeneity of response within KRAS driven NSCLC models for example ref. 21).The response of the cell lines used in the present study to single-drug treatments was previously studied comprehensively across >400 single agents25. The concentrations of the anchor drugs were chosen based on prior knowledge of on-target potency in cells and profile of response of these drugs across several hundreds of cell lines when available from prior studies (GDSC web site). A large-scale single-agent screen data was used to determine the dose of anchor drug that yielded very strong viability suppression in only a few cell lines . For EGFR inhibitors this would correspond to the highly sensitive cell lines that are dependent upon EGFR activity. The underlying assumption is that while the viability outcome of target inhibition varies across cell lines, a given drug will overall affect its target(s) equivalently across cell lines (barring drug pumps effects which in fact do not strongly affect the vast majority of drug responses in cells15). Indeed, while the complete lack of target expression is expected to ablate drug effects, previous large scale drug screening studies in cancer cell lines have shown that apart for a few exceptions concerning amplified genes, the RNA expression of the target is not a strong predictor of drug effects15 or siRNA mediated target depletion20. Thus, the anchor doses correspond to near complete suppression of target activity, which was for most targeted drugs ineffective in the majority of cell lines15. Similarly, for the library drugs, the concentration yielding strong viability suppression in only a few cell lines was determined based on single agent data or relevant literature. To further ensure that library drugs were suppressing their target(s) efficiently, one higher dose was added above this informed dose. Three additional lower doses were added to survey a larger breadth of target suppression. A dilution scheme of √10 was used (tenfold dilution every other dose). Drugs and concentration used are listed in Supplementary Data To identify active drug pairs across the 81 cell lines, 21 “anchor” drugs were selected based on their relevance to NSCLC treatment, approval status, results of preclinical therapeutic studies and biology. Those were combined with 242 “library” drugs covering most targeted therapeutic classes currently in use or in development against cancer. This 21 × 242 testing strategy was used in an ultra-high throughput screen in 1536 well plates using one fixed dose of anchor drug and five doses for each library drug Fig. , S2. FigThe screen was performed in technical duplicates with two sets of identical plates seeded on a given day: two DMSO anchored plates corresponding to single agent treatments and two anchor plates corresponding to combination treatments. Screening was repeated for plates that failed quality control based on coefficient of variation (CV < 25%) of the control wells . To collect data on all anchors, a given cell line had to be seeded repeatedly on different days. With the goal of minimizing noise in the dataset, single agent testing (DMSO as anchor) was repeated in parallel with each anchor to allow matched DMSO anchored plates and combination plates of the same drugging run to be compared. Thus, throughout the analyses, combination (anchor plate) and single agent (DMSO plate) data are compared using only plates matched by cell seeding date. Prescreening calibration of the cell density allowing for proper proliferation and good cellular enumeration was performed. Failure rate varied across cell lines but was overall low: in total, 5766 plates (1536 well plates) were used and 4223 passed QC requiring the cell population to double at least once in addition to acceptable CV of control wells. 73 cell lines out of 81 tested had a pass rate above 90% and only two had a pass rate below 50%. Thus, while a small number of combinations were not captured in the QC passed dataset for a minority of cell lines, the overall data coverage is high, and the vast majority of tests were performed at least in two technical replicates  of 0.80 and for technical replicates across combinations plates (Anchor drug + library drug) of 0.76. To evaluate the outcome and overall value of the screen data, a measure of synergy based on statistical independence of effect of the single agents was used (Bliss model): synergy was determined by considering the outcome of each single agent, taking the product of the single agent effects as the predicted outcome and comparing it to the experimentally determined viability outcome of the combination. The ratio between the expected and the observed outcomes constitutes the primary metric of synergy at each tested dose pair and an overall synergy score is derived from these five values (five doses of library drug combined with one dose of anchor drug). A synergy score <1 implies the drug combination is synergistic, with lower values indicating higher synergy . To complement this synergy score an efficacy gain score was also computed: The Higher than Single Agent (HSA) score describes the additional viability loss observed with a combination over the maximum viability loss observed with either of its components individually. HSA measure does not make any assumption regarding independence of effect of drugs or other assumptions built into various models of synergy26. Here, a negative HSA score implies the drug combination is more effective than the better of the two drugs (Methods). To confirm the validity of our drug interaction scores, empirical p values for synergy and HSA scores were also computed and a very strong correlation was found between those and the interaction scores . To obtain a ranking of synergistic drug pairs, two complementary strategies were initially used, leveraging the synergy score: (i) computing the median synergy score across all tested cell lines and (ii) the count of cell lines with a synergy score of 0.8 or less (see below how different synergy score impact the number of synergies observed across cell lines). Similarly, for HSA, a global score was obtained by either (i) taking the median of all HSA scores for that combination across cell lines or (ii) counting the number of cell lines passing a threshold of 15% HSA (loss of 15% of cellular viability compared to the viability obtained with most effective of the two single agents).To evaluate the quality of the data the correlation of viability values across technical replicates was computed. There was an overall good correlation across technical replicates with Pearson’s 29. The DNA damaging agents cytarabine, gemcitabine as well as the anti-metabolites pemetrexed and 5-FU also displayed HSA/Synergies in combination with AZD7762 albeit in fewer cell lines in the later cases than the former  kinases CHK1 and CHK2. Ranking combinations with AZD7762 based on the number of cell lines where the combination effect is either superior to single agents’ (HSA 15% or more) or synergistic (synergy score of 0.8 or less) shows that the inhibitor of WEE1, a kinase that regulates cell cycle checkpoint, is the top combination partner for AZD7762. Multiple synergies were also seen when combining ATR and CHK1/2 inhibitors  across cell lines for a given drug combination with the distribution of scores for all other drugs combined with the same anchor, using a Wilcoxon rank sum test. As a secondary measure, the median of the scores across cell lines for a given drug was compared to the median of scores of the rest of the drugs. The top combinations identified represent those with the highest effect across cell lines and thus perhaps across NSCLC patients. To further characterize the most promising combinations, the percent of cell lines with a synergy score within the top 5% of all scores  was also computed , probably the most well-known examples of synthetic lethal based treatments to date. Somatic mutations ond Fig. .34. Synergistic activity of RAF and MEK inhibitors combination has indeed been documented37. Here, the pan RAF  inhibitor AZ628 was the top combination partner for trametinib. The ERK inhibitor VX11e also yielded frequent synergies with trametinib  has been studied in combination across a variety of contexts. Feedback re-activation of the MEK pathway upon suppression of MEK or ERK impairs the clinical activity of BRAF inhibitorsnib Fig. . Consistway Fig. . Across way Fig. .PIK3CA, frequently mutated across multiple cancer types) was tested as an anchor drug. Combining BYL719 with PI3Kbeta selective inhibitors yields a strong HSA and synergistic effects across many cell lines with good consistency seen between the two PI3Kbeta inhibitors tested  and the MTORC inhibitor OSI-27 were also used as anchors: Pictilisib was broadly synergistic with trametinib, the ERK inhibitor VX11E and the mTOR inhibitor RAD001 (everolimus)  activator of PI3K amongst RTKs46.Drugs targeting the PI3K pathway also yielded interesting outcomes. The PI3K inhibitor alpelisib (BYL719) which targets selectively the alpha catalytic isoform of PI3K (encoded by us) Fig. , 4, 5C. 47. MEK and ERK inhibition were also seen as producing some synergies with Palbociclib. Selicicilb (CDKs) and I-BET (BRD) were the top combination in terms of number of synergies. HSA analysis confirmed BRD targeting drugs JQ1 and I-BET as some of the top combinations with palbociclib . Synergies with all three AURK inhibitors tested are strong and numerous suggesting on-target basis for the observed effects. Synergy between one of the AURK inhibitors tested here, alisertib and the HDAC inhibitor romidepsin was reported in T-Cell lymphoma51. Agents targeting metabolic enzymes were also good combinatorial partners for vorinostat: CPI613 (PDH/aKGH), Bromopyruvate (Hexokinase)  inhibitor MLN4924, the BET inhibitors I-BET and JQ1, the LSD1 inhibitor LSD1-C76 and the topo-isomerase I inhibitor irinotecan showed many synergies with vorinostat. These are well supported by literature in preclinical and for some, clinical studies  and multiple other tyrosine kinases. Multiple synergies were seen across inhibitors of the ERK and PI3K/mTOR pathways  Inhibitor, A832234. There is precedent for regulation of cell cycle and METAP targeting55. Some of the strongest observed synergies are only found in very few cell lines and are thus not flagged by the impact score analysis presented in , with only few anchors including OSI-027 (mTORC) and palbociclib (CDK4/6) displaying more synergies with seliciclib than dinaciclib. Because both dinaciclib and seliciclib inhibit CDK1/2/5/9 equipotently (at least in vitro59) it appears that secondary target(s) or perhaps differential mode of target engagement60 might be underlying the differences observed. Notably, we also find that AURK inhibitors had a clear tendency to be more broadly synergistic than CDK inhibitors even though both compound classes target kinases best understood as cell cycle regulators  affects synergy, the synergy patterns of drugs sharing some targets but differing in others were compared. First, we begin with some notable cases. Figure The use of 81 cell lines likely allowed us to robustly capture true synergistic events as well as estimate which combinations could be most relevant for disease treatment. To further test the robustness of our results and validate some of the key findings of our original screen, we carried out an independent screen of selected combinations . We aimed at confirming some of the top synergies observed in the original screen Fig. , as wellρ = 0.61, P = 0.00069; Supplementary Fig. ρ and Pearson’s correlation >0.25, respectively, Supplementary Data ρ and Pearson’s correlation >0.25, respectively; Supplementary Data We first analyzed how the two screen datasets compared in terms of best combinations across the tested cell lines. The validation screened confirmed the observations of the original screen Fig. . The ROCOverall, the validation screen confirmed the robustness of the finding in the original screen both in terms of top drug pairs identified as well as polypharmacology and differential synergy patterns observed between drugs sharing targets with common mechanisms of action.n ≥ 12) of the cancer cell lines than they are in the two immortalized cell lines . In addition, an analogous analysis using the HSA measure showed good consistency as well .We also mined drug combination data previously published in the DrugCombDB database62, which have tested five drug combinations across 36 NSCLC mouse PDX models. Although none of these drug combinations were exactly the same as those tested in our in vitro original drug combination screens, we were able to identify three drug combinations in Gao et al. that had similar drug targets to the drug combinations that we studied. These combinations show a similar percentage of high synergies or responders between the in vitro and in vivo data for PI3K and MEK inhibitors, PI3K and PIM inhibitors, IGF1R and MEK inhibitors (details in Supplementary Note We next compared the results of our drug combination screens to those obtained via drug combination experiments in NSCLC patient-derived tumor xenograft (PDX) models by ref. P = 3.58 × 10−34 one-sided Wilcoxon test). There is also a mild but highly significant correlation between the total number of targets involved in a combination and percentile of cell lines in which it is synergistic ,  are much less likely to be synergistic than combinations involving more than two targets (2), Fig. . Thus, dA bird’s eye view of the results of our screen  . Analysis of the effect of combinations (viability) in relation to the sensitivity observed with single agents revealed that the combination outcome is almost always contained within the range of sensitivity ever observed with single agents  Fig. .Fig. 7Re64. In the context of drug combinations, we define synthetic lethal (SL) interactions when neither of single agents are markedly effective, but the combination is . To quantify these SL effects in our screen, inactive single agents (and doses) are designated as those yielding a viability greater than 75% of control treatment (DMSO), but the combination is strongly effective (defined as below 40% viability). The number of such instances is very low, comprising just 1.32% of all possible combinations  mutant cell lines were seen to harbor more synergies than STK11 WT ones. Although TP53 encodes a major tumor suppressor and sensor of cellular stress, TP53 mutations were not associated with synergism (or lack thereof). This is reminiscent of the results obtained with single agent treatment of very large cell line collections, where TP53 mutational status is not a strong predictor of drug response15. The analysis of the genetics of synergism for each anchor individually revealed that three anchors, crizotinib (MET / RTK), phenformin (ET Complex V) and AZD7762 (CHK1/2) were statistically more synergistic (number of top 5% synergies) in the KRAS mutant than in KRAS WT . By contrast, six anchors promoted less synergies in STK11 mutant cell lines (KRAS WT and mutant) than STK11 WT ones: Tozasertib , linsitinib , luminespid , phenformin (ETC), nutlin (MDM2), foretinib . The combination of Bortezomib (proteasome) and AZD8055 (MTOR) was found to be more synergistic (using second-best synergy measure) in KRAS mutant than wildtype cell lines . Additional results are presented in Supplementary Data EGFR, 1128 for KRAS and 158 for PIK3CA mutated drivers . As expected, most of the combinations showing higher effectiveness in EGFR mutant vs WT cell lines contained the EGFR inhibitor anchor lapatinib (144 out of 195). Similarly, for PIK3CA mutants, the PI3Kapha inhibitor BYL719 is present in all (158 out of 158) combinations showing higher effectiveness in PIK3CA compared to the WT. Interestingly, for KRAS, only 35 combinations involving trametinib showed an imbalance in effectiveness. Olaparib , linsitinib , navitoclax  were anchors showing a high level of differential effectiveness in KRAS mutant versus WT models. Mutations in TP53 were associated with lower effectiveness for 199 combinations of which 184 involved nutlin . This is expected since nutlin is predicably ineffective in TP53 mutant cell lines14. Contrary to expectations, we find that KRAS models did not respond to combinations treatment significantly differently from the KRAS WT cell-lines. This observation is further confirmed by a principal component analysis of the post-treatment viability values showing that KRAS WT and KRAS mutant models do not segregate away from each other  and pictilisib (PI3K); they appear in 196 and 163 out of 684 combinations, respectively. For the MACF1 gene, the most differential anchor was AZD7762 (appears in 196 out of 217 combinations).To study the impact of the cell-line cancer driver genotype on drug combination outcome, the percentage of highly synergistic combinations (top 5% synergy rank) across different genotypes was analyzed. Overall, across all anchors and drugs, there was no statistical imbalance for synergism for any of the major cancer driver genotypes, including 70. Additionally, there was little difference in synergism or efficacy across subtypes of NSCLC . Phenformin was the only anchor showing a subtype imbalance, with adenocarcinoma models harboring more synergies than squamous cell carcinoma models .Overall, the brunt of the synergistic events is hence not accounted for by the mutational state of recurrent cancer driver genes. This is in keeping with previous studies of drug combinations reporting a rather idiosyncratic pattern of synergy across models tested when classified on genotype alone71 was queried. This revealed that the targets of highly synergistic drug pairs are closer in the PPI network than the targets of non-synergistic drugs    database STRINGst) Fig. . The samst) Fig. , in accoP < 0.05, Methods, Supplementary Data P = 0.56).To further evaluate the potential clinical relevance and benefit of the synergistic drug combinations identified here, a Cox regression analysis was performed using patient tumor data in TCGA. After controlling for single gene effects, age, sex, race, and cancer type among the 981 TCGA NSCLC samples, the concomitant down regulation of the genes composing 43 drug combinations (6.04%), were associated with an improved patient survival . However, we believe that these additional down-stream analyses are still of interest, as they identify the subset that is also associated with survival effects, as another potential indicator  of their putative translational value. Beyond that, we note that the lack of enrichment is not very surprising, given the well-known discord between in vitro and in vivo studies  and that not all drugs that are beneficial will necessarily show a survival benefit .To uncover what fraction of the synergistic combinations identified in the screen might arise from SL interactions between their targets , we employed the ISLE pipeline82 , but also supersensitive . Though the two classes are overlapping to some extent, we believe that each captures a different and important notion from a translational standpoint.Supersensitive combinations compose a different entity than synergistic combinations. The former denotes a specific class, where the combination achieves a response (in a subset of cell lines) that is higher than the highest response of any of the two drugs composing it observed across any of the cell-lines. Synergistic combinations, in difference, are combinations that achieve better response than the individual drugs composing them in many cell lines, but not necessarily better than the best individual cell-line response. Our interest in supersensitive combinations has been specifically motivated by recent work presenting the Independent Drug Action (IDA) principleIn summary, this work presents and analyzes the results of a very large dataset of drug combinations across lung cancer. The resulting dataset, that we make fully accessible to the scientific community, substantially expands on previous publicly available resources for drug combinations mining and modeling. There are many more analyses that could be performed using the data herein. Beyond that, our hope is that these data will foster the development of additional analyses and novel computational approaches advancing the prediction of drug-drug combinations outcome and our understanding of the rules underlying beneficial drug interactions in cancer.Cell line sources are listed in . Cells were seeded at densities optimized for proliferation based on pre-screen experimental determination in 1536 well plate format.Cells were seeded, placed overnight at 37 °C and drugs added the next day using a pin tool.After 5 days in drug cells were fixed permeabilized and the cells’ nuclei stained in a single step by adding a PBS Triton X100/Formaldehyde/Hoechst-33342 solution directly to the culture medium. Final concentrations: 0.05% TX-100/1% Formaldehyde/1 ug/ml Hoechst-33342. Plates were covered and placed at 4 °C until imaging. Imaging was performed on a ImageXpress Micro XL (Molecular Devices) using a 4 × objective. Cell nuclei enumeration was performed using the MetaXpress software and count accuracy was routinely checked visually during acquisition.No washing of plates was performed at any point post seeding.Viability was computed as the ratio of number of nuclei in the drug treated wells over those in the control (DMSO treated) wells. For the drug combination plates, the anchor drug was added to all wells. The relative viability  was then computed by dividing the number of nuclei in treated (drug combination wells) by the anchor drug alone wells. This viability was then compared to the viability computed from the single agent wells (DMSO as an anchor). This allows for direct comparison of drug effect without using the values of the DMSO only wells (in the DMSO anchored plate) to compute drug combination effect. While mathematically equivalent to the cross plate comparison, this approach allows to minimize data noise due to potential plate to plate cell seeding number variation. Quality control criteria included a CV of less than 25% of the control wells  and a cellular proliferation of at least one doubling. We combined replicates by taking the median values of the viability scores for each cell line and drug combination for each library drug dose. Proliferation was computed by comparing Day1 untreated plates (seeded concomitantly with the assay plates and fixed the day after seeding) to the DMSO only wells in the Day 6, DMSO anchor plates (Assay plates).We compute the HSA effect for each drug combination (on a cell line for a particular dose) by subtracting the combination viability minus the best (minimum) viability of the corresponding individual drugs. A negative value implies that the drug combination is more effective than the better of the two individual drug effects.Synergy scores are computed using the Bliss model  into z-scores and then computing empirical p values assuming a normal distribution. Similarly, empirical p values were also computed for HSA values for all cell lines and combinations . The ten lowest ranked cell lines are considered to be resistant cell lines for the combination. The ten top ranked cell lines are considered to be sensitive cell lines for the combination (excluding the most sensitive cell line). Now we check if the combination response (for library dose D4) in the resistant/sensitive cell lines is better than the best individual drug effect in the most sensitive cell line. If the combination effect is indeed better, we say that the combination makes the cell line super-sensitive. We considered all combinations which show super-sensitizer effect in at least 10% of the cell lines, and called them super-sensitizers.87, Selleckchem.com. The mapping is shown in Supplementary Data We mapped the drugs to their targets using several resources: DrugBank71 . We computed the PPI interaction score between the drug targets of any two drug combinations. If drugs have multiple targets, we either compute the max or median PPI score between the respective drug target pairs. We did this analysis using both the entire PPI network and by considering only drug target pairs which are bound to each other .We downloaded PPI network scores from the STRING databaseThe PPI score for the drug targets of the top synergistic combinations  was computed based on all PPI information types in STRING. The PPI scores for the synergies with high scores in at least 10% of the cell lines tested (637 combinations) were compared to those for the non-synergistic pairs . For multi targeted drugs the maximum PPI score across targets was considered. This is for the analysis in Fig. 89). The assumption being that the down-regulation of the target pair(s) may simulate clinical administration of the combination.To test whether clinical survival benefit could potentially be derived from treatment with synergistic drug combinations identified, we mined data from 981 TCGA NSCLC patients (lung adenocarcinoma and lung squamous cell carcinoma patients). Only combinations whose targets could be mapped to TCGA gene set and with less than four targets per drug were considered because a search across a large number of targets might show a survival signal by chance. There were 712 such drug combinations among the top 25% most frequently synergistic combinations. For these combinations, low expression (below 33 percentile) of at least one of all potential target pairs yielded an improved survival benefit after controlling for single gene effect, age, sex, race, and cancer type among the 981 TCGA NSCLC patients (both lung adenocarcinoma and lung squamous cell carcinoma patients75 which mine 981 TCGA NSCLC patients (lung adenocarcinoma and lung squamous cell carcinoma patients) to identify clinically relevant SL pairs. ISLE uses four different filters: (a) It firsts mines in vitro shRNA/CRISPR datasets spanning hundreds of cell lines to identify potential SL candidates; (b) It then looks for negative selection of co-inactivated gene pairs using gene expression and copy number analysis in TCGA cancer patients; (c) ISLE then selects gene pairs whose co-inactivation (low expression or copy number) is associated with improved survival; (d) It finally selects SL pairs where the genes have high phylogenetic similarity. More details of this method is explained in ref. 75. FDR threshold of 0.2 was used for this analysis. We mapped the drug combinations to their targets and mined for clinically relevant SL interactions between these target pairs. Only drug combinations where both the individual drugs have less than four targets are considered for this analysis. We identified experimentally derived SL gene pairs from various studies. The compilation of these various studies is provided in ref. 90. There are 27975 experimentally identified SL pairs . This will be our fraction of high synergies/HSAs for that library drug at some dose. We plot a density of these values for the fixed synergy/HSA threshold. We repeat the above procedure for different thresholds of synergy and HSA. We can also do the above procedure for a particular anchor drug.2 incubator. For pairwise drug-combination assessments in matrix format, compounds were acoustically dispensed into a 1536-well white solid bottom tissue culture-treated plate  with an Echo 550 acoustic liquid handler . A 9-point custom concentration range with 1:2 dilution between points was used for each drug-pair tested. Bortezomib  was used as a positive control for cell cytotoxicity. Cells were seeded into compound-containing plates at a density of 500 cells/well, in a final volume 5 µL of growth media by using a Multidrop Combi dispenser (Thermo Fisher). Plates were covered by a stainless-steel gasketed lid to prevent evaporation and incubated for 48 h in a humidified CO2 incubator. At the 48-h time point, 3 µL of Cell Titer Glo (Promega) was added to each well using a Multidrop Combi dispenser and plates were incubated at room temperature for 15 min with the stainless-steel lid in place. Luminescence readings were taken using a Viewlux reader (PerkinElmer) with a 2 s exposure time per plate. Viability of compound treated wells was normalized to DMSO and empty well controls present on each plate, and combination-response plotting was automatically performed for each individual drug+drug combination.All cell lines except for H2009 and A549 were purchased from American Type Culture Collection . H2009 and A549 were obtained from internal NCATS stock. Cell culture conditions were as follows:H727, H1437, H1666, H1703, H1734, H1755, H1915, H2347 and H2405 cells were grown in RPMI1640 medium (ATCC 30-2001); A549 cells were grown in F12K medium (ATCC 30-2004), A427, Calu-6 and SK-Lu-1 cells were grown in EME medium (ATCC 30-2003); H1651 and H2009 cells were grown in DMEM: F12 medium (ATCC 30-2006). All media was supplemented with penicillin/streptomycin and 10% fetal bovine serum (FBS). All the celllines were maintained at 37 °C in a humidified COThere are 29 drug combinations and 15 NSCLC cell lines in the validation screen, out of which there are 27 common drug combinations between the original and validation screen. The validation experiments were done in 10 doses × 10 doses in 4 different dilution series. high maximum × high maximum, low maximum × low maximum; high maximum × low maximum, low maximum × high maximum. From this HSA score and synergy score was computed for each. Because of this, the overall robustness of the outcome on a given combination is high and we take the best overall score for each cell line drug combination. An Excess_HSA score of less than −1000 was chosen as threshold to designate best combinations. The cell-line drug combination in the original screen was chosen to be highly synergistic if its second-best synergy score was in the top 20 percentile among the combinations considered for this analysis. Each of the common drug combinations , were ranked based on the percentage of cell lines (15 cell lines) in which they were highly synergistic. Spearman’s correlation between the ranking of the drug combinations in both the original and validation screens was then performed.ρ > 0.25 between the original screen and validation screens (the pertaining results are reported in the main text).For the 29 drug combinations , we tested for the concordance of drug combination sensitivity between the original screen and validation screen by measuring the correlation (both Spearman’s and Pearson’s correlation) between the viability scores across all 15 NSCLC cell lines between the two screens. In the original screen, as in most of our analysis, we considered library drug dose D4. For the validation screen, we considered the closest anchor and library dose concentrations that correspond to the corresponding doses used in the original screens. A similar analysis was also done when quantifying and analyzing the individual drug response. We also repeated the concordance analysis by removing outlier cell lines and considering only cell lines having a Spearman’s n ≥ 12) of the 15 NSCLC cell lines in comparison to their effectiveness in the non-cancerous cell lines. A similar analysis was done for synergy (ExcessHSA score). For computing statistical significance of either more effective or synergistic drug combinations in cancer cell lines in comparison to non-cancerous cell lines, we considered all the four replicates (block IDs) and computed one-sided Wilcoxon rank-sum test between viability or synergy scores between cancer and normal cell lines (FDR < 0.1).For the toxicity analysis, we tested the 29 drug combinations on two non-cancerous cell lines in addition to the 15 cancer cell lines. We have four block IDs that map to each drug combinations. For each block ID, we took the median viability score by considering all (10 × 10) doses. Then for each combination, out of four block IDs, we took the best (minimum) viability for every cell line. This forms the viability score for each drug combination in each cell line. We then identified drug combinations that are more effective (using viability scores) in at least 80% . Spearman’s correlation was computed between the two ranked lists.Matching the drug names and cell line names in our cohort with the DrugCombDB database91 to analyze differences in synergy or viability between two groups of cell lines. Multiple hypothesis correction (FDR correction) was done. Fisher exact test was used to test for enrichment. The code and data for main analysis has been made available for reproducibility. No data was excluded from the analyses. No statistical method was used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.We used statistical methods like Wilcoxon rank-sum testFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Dataset S1Supplementary Dataset S2Supplementary Dataset S3Supplementary Dataset S4Supplementary Dataset S5Supplementary Dataset S6Supplementary Dataset S7Supplementary Dataset S8Supplementary Dataset S9Supplementary Dataset S10Supplementary Dataset S11Supplementary Dataset S12


Syndactyly is one of the most common congenital upper extremity deformities. Syndactyly can be described as either simple, involving just the skin and soft tissue, or complex, involving the phalanges. Additionally, syndactyly can be categorized as complete, involving the entire digit (including the nail fold), or incomplete, which does not involve the nail fold. Multiple familial or spontaneous genetic abnormalities can cause syndactyly, and these mutations typically involve the canonical wingless-type (WNT) pathway. Surgical repair of syndactyly is typically done between six to 18 months of age, depending on the type of syndactyly. Regardless of the classification of the syndactyly, the repair is performed before school-going age .One or more imaging modalities are used to aid the surgeon in deciding the surgical approach for the syndactyly repair. The surgical plan must be clearly communicated with parents to manage expectations of aesthetics and function of the digits post-surgery. In brief, a syndactyly release surgery involves the creation of the web space using a geometrical design of the surgeon’s choice, defatting of finger flaps, separation of the digits, and closure with absorbable sutures. However, the approach may vary depending on the patient.A “best” approach for rectifying the difference in surface area of separated versus fused digits has not yet been determined. While this was typically done using a skin graft, the use of alternative methods  has been on the rise given the undesirable side effects of a graft. Less commonly, an external fixator can be used to expand soft tissue and skin. In the case of complete syndactyly, the Buck-Gramcko technique is most commonly used for nail flap reconstruction. Complications of the surgery include contracture, web creep, and the need for a second surgery. Thus, parents must be counseled in recognizing signs of complications. Syndactyly is a congenital condition that involves the fusion of either one or multiple adjacent digits . SyndactTypes of syndactylySimple syndactyly occurs when there is a fusion of the skin and soft tissue, while complex syndactyly also involves the fusion of adjacent phalanges . About 1Common conditions associated with syndactyly include Poland and Apert’s syndrome, although syndactyly is a feature of over 300 different syndromes . As caseEmbryologyMost cases of syndactyly have a genetic basis, with 10% to 40% of cases presenting with a family history of syndactyly . Hand deTiming of surgerySimple syndactyly is usually repaired between 12 to 18 months of age, while complex syndactyly or simple syndactyly where fused digits have a significant difference in length should be repaired before six months of age ,9,14,15.Surgery indicationsSyndactyly is a clinical diagnosis; however, a radiograph must be performed for not only confirmation but also to determine the presence of hidden polydactyly or other abnormalities within the web space. Magnetic resonance imaging or ultrasound may also be indicated to determine tendon and/or muscle involvement to decide the best course of action for surgery . A hand Contraindications for surgeryAlmost all cases of syndactyly indicate that surgery should be performed. Some contraindications include extremely mild cases of syndactyly where the function is not impaired or certain cases of extremely complex or complicated syndactyly . This coSurgical techniquesVarious techniques are used in syndactyly release depending on the nature of the syndactyly. The most common techniques are outlined below. A dorsal flap is typically used in the creation of the web space. Several geometrical designs can be used for the dorsal flap, including but not limited to rectangular, triangular, multilobed, and many others also being used ,17. An eThe sides of the digits are usually separated using zig-zag folds. Whichever technique is used, it must be marked on the dorsal side, with a mirror image marking on the volar side. Finger flaps are defatted prior to closure to prevent suture tightening and ischemia. Great care must be used to take note of the neurovascular bundles while making incisions to ensure that they are well protected . FingersSkin Flap with GraftThe surface area of separated digits is often much greater than that of fused digits. A graft must be used if the surface area is greater than 22%, as skin alone will not be able to rectify this difference ,9. This Skin Flap with No GraftIn the last two decades, there has been a shift towards avoiding the use of a skin graft, especially due to the inconsistent growth rate of the transplanted skin causing scarring and contracture. This method is indicated if the syndactyly is simple, incomplete, and does not extend past the proximal interphalangeal joint . To overExternal FixatorWhile not well studied, there have been instances of surgeons using an external fixator, especially in patients with simple complete syndactyly. Once Kirschner wires are added from the tip to the base of each fused digit, an external fixator is applied . It is pNail Flap and Fingertip ReconstructionThe Buck-Gramcko technique is used when there is nail involvement, as would be the case in complex syndactyly. A dorsal incision is made on the nail, and lateral digital flaps are made on the distal pulp . The flaPost-surgical carePostoperative dressings are imperative to provide compression along the grafted areas and also between digits to allow for separation. In particular, moist cotton is placed between the newly separated digits, after which the extremity is reinforced with gauze. In younger patients, it may be indicated to reinforce the gauze applied after surgery with a plaster of Paris (POP) cast . The POPSurgery complicationsMost surgical complications are associated with cases of complex or complicated syndactyly. These cases have a higher incidence of postoperative ventilator dependence, contracture, web creep, and hematomas . Simple Syndactyly is a common congenital hand deformity that can manifest in an isolated or syndromic form. Patients can present with simple syndactyly, which occurs when the web space moves distally and there is a fusion of the soft tissue and skin but not of the nail bed, or with complex syndactyly, where there is bone involvement in addition to skin and soft tissue involvement. Patients can also present with either complete syndactyly, which occurs when there is the involvement of the entire digit, or with incomplete syndactyly, where only a portion of the digit is involved. Generally, syndactyly release will occur during school-going age . There should be several separate surgeries if one digit is involved in multiple fusions, as performing both releases in one surgery can lead to neurovascular compromise. Several techniques have been used to separate the syndactylous digits, including the use of the graft, not using a graft, using synthetic dermal substitutes, or application of an external fixator to stretch the skin and soft tissue. Gauze will be placed between the digits post-surgery, which may or may not be reinforced with a POP cast. Complications such as contraction, hematomas, and necrosis may occur after the syndactyly release. If complications such as web creep occur after surgery, another surgery may be indicated. The surgeon must maintain strong communication with parents to explain the surgical plan, postoperative care, and signs that complications are occurring.


Unlike genomic alterations, gene expression profiles have not been widely used to refine cancer therapies. We analyzed transcriptional changes in acute myeloid leukemia (AML) cell lines in response to standard first-line AML drugs cytarabine and daunorubicin by means of RNA sequencing. Those changes were highly cell- and treatment-specific. By comparing the changes unique to treatment-sensitive and treatment-resistant AML cells, we enriched for treatment-relevant genes. Those genes were associated with drug response-specific pathways, including calcium ion-dependent exocytosis and chromatin remodeling. Pharmacological mimicking of those changes using EGFR and MEK inhibitors enhanced the response to daunorubicin with minimum standalone cytotoxicity. The synergistic response was observed even in the cell lines beyond those used for the discovery, including a primary AML sample. Additionally, publicly available cytotoxicity data confirmed the synergistic effect of EGFR inhibitors in combination with daunorubicin in all 60 investigated cancer cell lines. In conclusion, we demonstrate the utility of treatment-evoked gene expression changes to formulate rational drug combinations. This approach could improve the standard AML therapy, especially in older patients. Drug combinations provide an effective way to tackle the resistance of cancer cells to individual drugs and to reduce side effects by means of dose reduction. Taking acute myeloid leukemia (AML) as an example, the combination of cytarabine and daunorubicin forms the first-line induction therapy that achieves complete remission in 80% of the patients . But mosThe typical approach to identifying drug combinations includes high-throughput in vitro and/or ex vivo drug cytotoxicity screening. However, the experimental evaluation of hundreds of drugs and their combinations is laborious. Hence novel predictive methods are required to shortlist the most promising candidates that can be verified experimentally. Gene expression profiles have been underutilized in clinical practice to formulate drug combinations compared to somatic mutations. For example, the standard AML therapy is supplemented in carriers of FLT3, IDH1/2, and BCL2 mutations by targeted drugs . HoweverHowever, such gene expression-based markers are still far away from clinical implementation. Crucially, their utility in treatment refinements has not been explored, although it is known that the transcriptional effects contribute to the drug’s anticancer activity . The mosIn the present work, we analyzed the transcriptional response of AML cells to first-line drugs cytarabine and daunorubicin as monotherapies and in combination by performing RNA sequencing. By comparative analysis, we identified response-defining gene expression signatures that were mimicked pharmacologically to identify potential novel daunorubicin-based combinations.We treated HL60, MOLM13, and OCIAML3 cells with cytarabine (0.1 µM), daunorubicin (0.02 µM), and their combination (0.1 µM cytarabine + 0.02 µM daunorubicin) for 24 h. CellTiter-Glo-based cell cytotoxicity measurements revealed differential sensitivity of these cells to individual drug treatments. HL60 cells exhibited the lowest response to daunorubicin treatment (percentage cell death 7 ± 7.02), while OCIAML3 did not respond to cytarabine treatment A. On theTo understand the transcriptional basis for drug sensitivity and synergy, we quantified gene expression by performing total RNA sequencing after treatment with cytarabine and daunorubicin as monotherapies and after the combination therapy. We compared these profiles with DMSO-treated cells to identify the gene expression changes. Most transcriptional changes in AML cells were gene inductions (average 79%); as opposed to gene repressions (average 21%). This proportion was consistent across both individual treatments and the combination treatment . The total number of differentially expressed genes correlated with the percentage of cell death in response to drug treatments. HL60 cells responded least to the daunorubicin treatment and exhibited the least number of differentially expressed genes (1806) compared to cytarabine (3552) and the combination (4608). The highest number of differentially expressed genes in response to the combination was associated with the highest cytotoxicity A,B.Similarly, OCIAML3 cells with no response to cytarabine treatment exhibited only six differentially expressed genes. MOLM13 cells also exhibited an increasing number of differentially expressed genes with increasing cytotoxicity to cytarabine, daunorubicin, and the combination. This indicated that the cytotoxic response was reflected by the transcriptional response of AML cell lines to the treatments.To further understand the role of gene expression changes in drug-mediated cytotoxicity, we investigated the down-regulation of the genes essential for cell survival. We obtained the list of genes essential for the survival of HL60, OCIAML3, and MOLM13 cells from an RNAi-based cell viability screen DepMap ,15. SeveTo investigate the similarities between standard AML drugs and their combination at the transcriptional level, we compared the differentially expressed genes across the three AML cell lines. There were, on average 4% down-regulated and 20% up-regulated genes common to all treatments . In HL60The comparison between different AML cell lines revealed few similarities. There were 0%, 0.1%, and 0.1% common down-regulated genes and 0%, 2.9% and 2.8% common up-regulated genes after cytarabine, daunorubicin and the combination treatment, respectively, across 3 AML cell lines . RegardiBecause the transcriptional effect of standard AML drugs and their combination was unique to every cell line, we investigated the associated biological processes to understand further the mechanism of action of individual drugs and the synergy between them. We performed functional enrichment analysis using the clusterProfiler R package to identify biological processes affected by the treatments. The specific pathways were considered to be affected by the treatment if they were affected in all three cell lines by the combination treatment or affected in at least two cell lines by individual treatments. The reasoning behind this approach was that we observed the cytotoxic response to the combination treatment by all cell lines, but at least one cell line was resistant to the individual treatments.We observed the involvement of processes related to cell adhesion and migration, cell activation, chemotaxis, cell differentiation, IL6 production, and phosphorylation after treatment with both individual treatments and the combination treatment in all 3 AML cell lines A. We furWe then formed a network map of daunorubicin-affected biological processes by clustering overlapping gene sets. The clustering showed that the processes related to leukocyte activation and differentiation were predominantly affected by the daunorubicin treatment in MOLM13 and OCIAML3 cells . InteresTo further understand the lower response of HL60 cells to daunorubicin treatment, we compared its transcriptional response with that of the daunorubicin-sensitive MOLM13 and OCIAML3 cells. We constructed profiles of gene expression changes depicting either response (DNRResponse) or resistance (DNRResist) to daunorubicin treatment. These profiles were constructed assuming the gene expression changes associated with a positive treatment response would have only occurred in daunorubicin-sensitive MOLM13 and OCIAML3 cells. Conversely, the gene expression changes related to a lack of treatment response would have only occurred in daunorubicin-resistant HL60 cells. DNRResist profile consisted of 132 gene inductions and 64 gene repressions, which occurred in HL60 cells after treatment with daunorubicin only and were absent after both daunorubicin and the combination treatment in the other two cell lines . On the We then hypothesized that the response of HL60 cells to daunorubicin treatment could be enhanced by inhibiting the DNRResist profile and mimicking the DNRResponse profile by adding appropriate drug combinations. To predict the most suitable combination partners, we compared the DNRRes and DNRResponse profiles with the gene expression changes associated with other drug treatments using the CLUE platform. We specifically searched for the drugs with opposite transcriptional responses as that of the DNRRes profile and similar transcriptional responses as that of the DNRResponse profile. We discovered that the transcriptional responses to HDAC, tubulin, RAF, P38 MAPK, and PARP inhibitor were similar to DNRRes, while the response of EGFR, MEK, aurora kinase, and HSP inhibitor and calcium channel blocker was opposite to that of the DNRRes signature . We predTo validate the predicted combinations, we measured the cytotoxic response of HL60, MOLM13, OCIAML3, and primary AML cells to PD98059, a MEK inhibitor, daunorubicin, and their combination. This was compared with the combination of vorinostat, an HDAC inhibitor and a non-ideal candidate combined with daunorubicin A. The CeOn the contrary, OCIAML3 cells exhibited an antagonist response at a lower concentration of PD98059 , there were 59 genetic alterations with significant association with increased daunorubucin-EGFR inhibitor synergy . Among themicals . We furthemicals . Among tWe assessed the effect of the most widely used first-line AML drugs, cytarabine and daunorubicin, on transcription profiles. This led to the identification of EGFR and MEK inhibitors as the most suitable combination partners for daunorubicin. These combinations had a synergistic effect when combined with daunorubicin, with minimal toxicity when deployed alone. These findings underscore the importance of transcriptional response in drug-mediated cytotoxicity and their utility in formulating better drug combinations. Further preclinical investigations are needed to compare personalized gene expression changes-based combinations with standard combinations which could expand the treatment options for older AML patients that are not eligible for intensive therapy or for relapsed cases.Cytarabine and daunorubicin, the most widely used AML drugs, interfere with DNA synthesis in proliferating cells. Cytarabine, like deoxycytosine, gets incorporated in DNA and causes single-strand breaks during the S phase. Uncorrected single-strand breaks are converted to double-strand breaks leading to cell death. Daunorubicin inhibits the topoisomerase II enzyme leading to double-strand breaks, which also cause cell death. Mechanistically, gene repressions would be expected to reflect direct DNA damage at the pertinent gene loci.In contrast, we observed the predominance of gene inductions in response to cytarabine and daunorubicin treatments. The transcriptional response to DNA damage is highly specific to the type of drug and to the resulting nature of the DNA damage. In the case of UV irradiation and bulky adducts, local and global transcriptional reduction is observed because of RNA polymerase II suppression via post-translational modifications and proteasomal degradation ,17. On tPathway analysis revealed the activation of treatment-relevant processes, including chemotaxis, leukocyte migration, differentiation, and migration. Along with its cytotoxic effect, cytarabine has been known to induce AML cell differentiation at lower doses . We alsoTP53 in HL60 cells, a major regulator of the DNA damage response [TP53 can sustain cell growth via activated RAF/MEK/ERK signaling [Interestingly IL-6 was not associated with HL-60 cells which exhibited poor response to daunorubicin treatment. However, these cells showed IL-6 activation after the combination treatment. This indicates the involvement of proinflammatory cytokines in response to standard chemotherapeutics. The standard “7 + 3” AML treatment activates pro-inflammatory cytokines . NF-kB sresponse . Loss ofignaling . This isignaling . This maInstead of targeting the aforementioned processes individually, we derived a comprehensive profile of gene expression changes depicting either response or the lack of response to daunorubicin in AML cells. By comparing gene expression changes after individual treatments and their combination across all three AML cell lines, we identified potential daunorubicin combination partners. Experimental validation confirmed the synergistic response of EGFR and MEK inhibitors. EGFR, a tyrosine kinase, is known to be involved in various cancers, including breast and non-small cell lung cancer, because of its proliferative and anti-apoptotic activities . EGFR isMAP-ERK kinases (MEKs) are serine/threonine kinases that respond to stress signaling and are involved in cell survival and apoptosis . MEKs arFLT3, IDH2, and NRAS. This indicates that the synergistic effects of the daunorubicin and EGFR inhibitors could be applicable to a wide number of AML samples. In most cases, the targeted drugs are insufficient as a single agent and need to be combined with existing classical drugs [IGF1R and PRKCD mutations associated with increased sensitivity to the combination. Insulin-like growth factor 1 receptor (IGF1R) is a tyrosine kinase associated with cell growth and survival. It is also involved in the DNA damage tolerance pathway by rescuing DNA damage-stalled replication fork [PRKDC mutation to the daunorubicin-EGFR inhibitor combination could result from inhibiting nonhomologous end joining-mediated DNA double-strand breaks repair [Currently, gene mutations play a critical role in guiding the choice of AML drugs ,42. Our al drugs . Hence, ion fork . Proteinion fork ,45. The s repair . To summAn apparent limitation of this work is the use of a small cohort of AML cell lines. However, this approach better represents a clinical scenario where individual patients need to be evaluated for the best possible drug treatment. Crucially, the combinations identified in our study were experimentally confirmed as synergistic. Furthermore, the analysis of previously published drug combination screens confirmed the synergistic effect of the combinations beyond the cell lines in which they were predicted ,47. Such2 in appropriate media  containing 10% heat-inactivated FBS  and OCIAML3 in alpha-MEM medium  containing 20% heat-inactivated FBS). Cell lines were routinely verified for mycoplasma contamination using Venor®GeM Mycoplasma Detection Kit . Cell lines were authenticated by Multiplexion, Germany. The inhibitors cytarabine, daunorubicin, and vorinostat were purchased from Selleckchem . The MEK inhibitor PD98059 was purchased from Merck, Darmstadt, Germany.Acute Myeloid Leukemia (AML) cell lines HL-60, MOLM-13, and OCI-AML3 were purchased from Deutsche Sammlung von Mikroorganismen und Zellkulturen . Cell lines were maintained at 37 °C and 5% COHeparin-treated bone marrow samples were obtained from patients with AML treated at the University Medical Center . Informed consent was obtained in accordance with the Declaration of Helsinki, and all the laboratory experiments using primary samples were approved by the local ethics committee. Mononuclear cells were maintained for 48 h in Iscove’s modified Dulbecco’s media containing BIT (15%), beta-mercaptoethanol (20 µM), stem cell factor (100 ng/mL), FLT3-L (50 ng/mL), interlukin 3 (20 ng/mL), GM-CSF (20 ng/mL), thrombopoietin (50 ng/mL), stemregenin 1 (0.75 µM), UM171 (35 nM), and penicillin-streptomycin (1%) [® 2.0 assay  according to the manufacturer’s instructions. In short, 5000 cells were seeded per well in a 96-well plate containing 100 µL media and treated with cytarabine (0.1 µM) and daunorubicin (0.02 µM) alone and in combination for 24 h. After the treatment, 100 µL Glo reagent was added to each well, and chemiluminescence was measured after 10 min incubation in the dark at room temperature. The percentage of cell death was calculated by comparing the luminescence of drug-treated cells with the DMSO-treated cells (considered 100% viable).Cell viability in response to drug treatments was measured using CellTiter GloThe effect of drugs cytarabine and daunorubicin on AML cell transcription profiles was determined by RNA-Sequencing as described before . In shorp-adjusted values below 0.05 were considered significant.The quality of raw sequencing reads was assessed using FastQC version 0.11.9  . The traTo identify transcription profile-based drug combinations, gene expression changes after monotherapy and combination therapy were compared across 3 AML cell lines. We reasoned that by comparing treatment-evoked gene expression changes in responsive cells with non-responsive cells, we could enrich sensitivity-defining genes that could be targeted to enhance the cytotoxic response. The daunorubicin-resistant (DNRResist) profile was derived from HL60 cells using gene expression changes after only daunorubicin treatment which was absent in MOLM13 and OCIAML3 cells after both daunorubicin and the combination treatments. Analogously, the daunorubicin-response (DNRResponse) profile was constructed using gene expression changes common to MOLM13 and OCIAML3 cells which were absent in HL60 cells after both daunorubicin and the combination treatments. The DNRResist and DNRResponse profiles were submitted to the online CLUE platform to identify other drugs and genetic modulations with similar transcriptional responses . NormaliTo validate the predicted combination partners, the viability of HL60 cells was measured in response to the combinations of PD98059 (MEK inhibitor) and vorinostat (HDAC inhibitor) with daunorubicin using a 5 × 3 concentration matrix. Similarly, the viability of MOLM13, OCIAML3, and a primary AML sample was measured in response to the combination of PD98059 and daunorubicin. The cell viability was calculated using the CellTitre Glo assay described above (section CellTiter-Glo cell toxicity assay). The effect of the combination was evaluated using the Bliss independence test of the SynFinder portal . The comp-values were corrected using the Tibshirani-Storey method implemented in the GDSCTools python package (version 1.0.1) [p-value (0.001) and FDR (25%) was used to identify significant associations. The ten most frequently mutated genes in AML patients were obtained from cBioPortal [To investigate whether the mutational background of cancer cell lines influences the synergistic response of gene expression-based drug combinations, we correlated genomic markers by fitting an ANOVA model ,60. We on 1.0.1) . The defioPortal ,64. The ioPortal . Based o


Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades.To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug–cell line pairs.The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos.  Tumors are highly plastic and heterogeneous entities composed of many different cell types, with distinct expression and metabolic profiles. Consequently, tumor plasticity is linked to drug resistance, as some cancer cells may survive, divide, and give rise to a resistant cell population, ultimately leading to disease recurrence. The average discovery and development process is estimated to take over a decade and $2 billion for each drug  (50) of drug–cell line combinations by learning from different omics types from cancer cell lines and the structural features of drugs. We also evaluated the influence of using scRNA-seq data, in addition to other data types, on the prediction performance of DELFOS.Given the large amounts of different types of data available and the need for better computational tools to assist in drug development, we developed a novel Deep Learning (DL) model for drug sensitivity prediction in cancer cell lines, named Drug Efficacy Leveraging Forked Optimized and Specialized networks (DELFOS). Specifically, our algorithm can be used to predict the ln(IChttps://www.ncbi.nlm.nih.gov/geo/), except for the data from https://figshare.com/s/139f64b495dea9d88c70).All scRNA-seq data were retrieved from publicly available datasets. Five scRNA-seq datasets describing gene expression in different cancer cell lines treated with 0.1%–0.2% DMSO were collected . All datAll scRNA-seq data were annotated and processed using Seurat v3 R library . Datasethttps://depmap.org/portal/ccle/), and included protein-coding gene expression, copy number variation, chromatin profiling, miRNA expression, and methylation data. Finally, drug screening data were downloaded from the GDSC website (https://www.cancerrxgene.org/downloads/bulk_download), containing ln(IC50) values for drug–cell line pairs. Only the more recently available dataset (GDSC2) was used.Five datasets of different omics data from several cancer cell lines were retrieved from the CCLE 2019 and DepMap Public 22Q2 public releases in the DepMap Portal  were retrieved from PubChem using PubChemPy . Unfortu50) values were averaged. To match these datasets with available single-cell data, only cell lines with available scRNA-seq data were selected.Except for the scRNA-seq datasets, all other datasets followed a similar preprocessing protocol to prepare them as inputs for the model. First, 10% of the unique cell lines and drugs were randomly selected as part of the validation datasets, hereafter referred to as leave-cell-out and leave-drug-out datasets, respectively. Once the validation cell lines and drugs were extracted, the remaining drug–cell line pairs were further split into training and testing datasets according to a 70–30 train-test split. Features that displayed zero variance were removed as they did not contribute to the prediction. All datasets were finally standardized by first calculating the mean and standard deviation values from the training data and then transforming the datasets based on such values. Because not all datasets from CCLE had the same data available for all cells, there was a minority of missing values that were replaced with zeros, as previously reported  , root-mean-square error (RMSE), Pearson correlation coefficient (PCC), Spearman’s rank correlation coefficient (SCC), and XGBoost)  and anotOnce all datasets were processed and prepared to match the features from drug data, bulk phenotypic data, and scRNA-seq data, 66 cancer cell lines from 16 different tissues and 228 unique drugs were used to train and evaluate model performance . Most ce50) values provided by the GDSC2 dataset, the target variable for DELFOS, displayed an interquartile range of 3.33 with a median of 3.57 (50) was 3.14, which was somewhat lower than the median given the presence of many small ln(IC50) values. Compounds with very small average ln(IC50) values were effective in eliminating cancer cells and were considered powerful anticancer agents, as expected (50) values were the main antioxidant agents. There were also anticancer agents among the top 10 compounds with increased average values. This is not unexpected given that these compounds are often more effective against specific types of cancer.The ln prediction  for the drug–cell line pairs when presenting the descriptors of an unseen drug. Accordingly, the correlation metrics were not optimal, with a PCC of 0.53, SCC of 0.58, and an R2 of 0.24. Similarly, the error metrics suggest an increased average deviation between the predicted values and the ground truth.Following hyperparameter optimization and training, the performance of our models was evaluated using the leave-cell-out and leave-drug-out datasets for ln for the unseen cell lines. Similarly, DELFOS performed better than XGBoost when presented with unseen drug data. In contrast, Precily displayed better results than DELFOS for PCC and SCC but not for R2. This suggests that Precily generated predictions with stronger monotonic correlations with the ground truth values, although these values did not match the data or predictions by DELFOS. Nonetheless, none of these models, including DELFOS, could optimally predict ln(IC50) when unseen drug data were presented, highlighting a problem to be addressed in future DRP approaches.Our model also outperformed XGBoost and Precily when evaluated using a leave-cell-out dataset. Precily’s results were suboptimal, suggesting that the model could not effectively predict ln values for drug–cell line pairs, even for cell lines that have never interacted before. DELFOS matched or outperformed several recently published DRP algorithms trained using data from GDSC and CCLE. We expected that including scRNA-seq data in the pipeline would improve the prediction performance, particularly for underrepresented tissues. However, we observed no significant benefit when incorporating these data, at least when using this approach. Owing to high variability and noise, scRNA-seq data may yield no patterns with sufficient correlation to drug response to improve the overall prediction performance. This also suggests that the model could receive too much cell data and become overfitted to cell features, which can be amplified when scRNA-seq is provided. More data are required to identify better alternatives for preparing single-cell data for ML. Missing values may also contribute to this, particularly in the CCLE methylation dataset, which displays a relatively high number of missing values. Moreover, our model suffers from low performance when receiving data from unseen compounds. This was also observed by other authors  it was very recently published and (ii) it follows a streamlined approach that had good performance in its original study . Most stOverall, DELFOS performed quite well compared to recent algorithms and could leverage multi-omics data for the prediction of drug responses in several cancer cell lines.btad645_Supplementary_DataClick here for additional data file.