Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection. Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models. Contemporary therapeutic strategies use cancer drugs, with lower toxicity that specifically target aberrantly expressed or mutated proteins and, in general. EGFR expression and mutations, KRAS mutations, BCR-ABL fusions, and HER2 overexpression are such examples of common therapeutic targets in cancer3. Unfortunately, not all cancers and anti-cancer drugs are known to be associated with strong targetable genetic biomarkers. As such, it is concluded that the simple relationship of drug targets or mutational status alone is incomprehensive for predicting the efficacy of specific targeted therapies5. Furthermore, administering a targeted therapy without considering drug resistance as a consequence may lessen patient survival. The drug resistance might be manifested through clonal expansion under treatment-induced selective pressure or from alternative signaling pathways that sustain tumor growth6. As such, early inference of drug response based on pretreatment molecular portraits of cancer has become a necessity7.Cancer is a highly complex disease exhibiting varying degrees of genetic and phenotypic heterogeneity within individuals. Despite the apparent overall improvement in prognosis, responses to cancer treatment are often unpredictable. This is primarily attributable to the clonal diversity of cancer cells and associated phenotypically altered non-malignant cells in the tumor microenvironment. These pose a substantial hindrance to the optimal therapeutic management of the disease2. Cancer Cell Line Encyclopedia (CCLE),8 Genomics of Drug Sensitivity in Cancer (GDSC)9, and Cancer Therapeutics Response Portal v2 (CTRPv2)10 are noteworthy among these. These high-throughput screening studies constitute an expansive knowledge base comprising high-throughput screening studies entailing more than 1000 cell lines and several hundreds of anticancer drugs2. Concurrently, The Cancer Genome Atlas (TCGA)11 serves as another rich database featuring gene expression profiles of mostly primary tumors spanning multiple cancer types, with associated clinical metadata and drug response annotations. This wealth of data has enabled drug response modeling based on molecular profiles. Various machine learning methods have been proposed for drug response prediction in cancer. Jia et al. proposed a deep variational autoencoder for imputing drug response through compression of multiple genes into latent vectors in low dimensional space12. Ammad-Ud-Din, Muhammad, et al.13 reported a kernelized bayesian matrix factorization-based drug response prediction through incorporating prior knowledge of pathway-drug associations. Another approach utilized matrix factorization with similarity regularization for drug response prediction in cell lines by employing chemical structures of drugs and gene expression profiles14. Chang and colleagues proposed CDRscan, a convolutional neural net that leverages mutational signatures for predicting drug effectiveness15. Sakellaropoulos, Theodore, et al. reported gene expression data based deep neural network for drug response prediction that outperforms ElasticNet and Random Forest16.In recent years, the availability of large-scale pharmacogenomic databases has propelled predictive personalized oncology research18. Notably, in our previous works, we have shown how pathway projection facilitates better modeling of biological processes19. As an added advantage, data integration based on pathway enrichment scores mitigates batch-effects20. While single-cell RNA-seq (scRNA-seq) enables the characterization of cellular heterogeneity in tumors, there is very little visible effort to leverage this fine-grained molecular information to predict drug response at sub-clonal resolution. This is primarily because most available training data, as indicated above, are bulk expression profiles, and training on bulk RNA-seq and testing on scRNA-seq is expected to give rise to misleading predictions. Pathway projections of scRNA-seq/bulk RNA-seq profiles reasonably alleviate this problem. Notable in this regard is the work by Suphavilai, Chayaporn, et al.,21 that describes a drug response prediction approach in head and neck cancer, leveraging scRNA-seq profiles. The authors, however, did not explore the utility of drug descriptors to generalize the prediction model.By carefully surveying these methods, we identified two critical scopes for improvement. First, most of the past studies do not consider the structural properties of drugs as features (explanatory variables in predictive tasks). As a result, the machine learning models learn suboptimally and fail to make predictions on drugs that are not part of the training data. Second, gene expression levels are considered as independent variables, ignoring their pathway-specific combinatorial implication. Since most targeted therapies work through pathways, disregarding the pathway resolution causes overemphasis on machine learning techniques. Past studies have demonstrated the utility of using pathway enrichment scores for various downstream analyses, as opposed to gene expression values22 and obesity23, respectively but also are found to have therapeutic potential in PCa. Finally, we used tumour RNA-seq data and the recorded clinical treatment response information from TCGA to validate the possibility of extrapolating our approach in precision oncology. We benchmarked the efficiency of the model, trained on patient samples from TCGA on RNA-seq profiles of pre-treatment, and matched post-relapse drug-resistant BRAF mutant melanoma patients. Our study connects a systematic drug response prediction pipeline with layered in vitro and in vivo comparisons involving cell lines, xenografts, and patient data, which is the most important prerequisite for the clinical implementation of such approaches.In this work, we developed a deep neural network (DNN) based framework named Precily to predict drug response in both in vitro and in vivo settings. For model training, we made use of cell line-based high-throughput screening data . Convinced by the reproducibility and overall performance of the cell line model on unseen data (bulk and single-cell RNA-seq), we explored similar prediction tasks pertaining to in-house prostate cancer (PCa) cell lines and animal models under differential treatment conditions. As a proof of principle, we first evaluated Precily on differentially treated PCa cell lines. To ensure the cross-sample predictability of our model, we examined our LNCaP xenograft dataset mirroring in vitro treatments. For this, we utilized LNCaP xenografts from a PCa progression study where the tumors were harvested at different stages of treatment resistance. Precily predictions revealed clinically and biologically relevant associations of drugs and pathways in the context of treatment resistance and sensitivity. We also evaluated the utility of Precily in predicting response for drugs that had never been seen by the training models. For this, we considered metformin and orlistat, which are used for the treatment of type 2 diabetes24. Second, we obtained numeric molecular descriptors for 173 anti-cancer compounds (reported in GDSC) using SMILESVec25, by supplying simplified molecular-input line-entry system (SMILES) notation, retrieved using PubChemPy26. The summary statistics of the dataset used are provided in Supplementary Table aka. independent variables), while LN IC50 estimates as the decision variable (aka. dependent variable) under a regression framework. Both explanatory and decision variables are continuous in nature. The samples can be best understood as  tuples with their responses. Here cell lines are encoded by pathway enrichment scores as discussed above  tuples introduces data leak issues that do not mirror practical applications. In this way, training data becomes privy to cell line gene expression profiles and its sensitivity to some drugs, thereby making it rather easy to predict its sensitivity for a new drug. An analogous possibility is unlikely in clinical settings. Inferring drug responses in patients cannot be prejudiced upon accounts of past responses. In such a scenario, treatment naive cases cannot be tackled. To this end, we split the dataset based on the cell lines so that no cell line is common among the training, validation, and test sets. We compared our framework, named Precily, with two well-cited methods — Cancer Drug Response prediction using a Recommender System for single-cell RNA-seq (CaDRReS-Sc) by Suphavilai, Chayaporn, et al.21 and another method by Sakellaropoulos, Theodore, et al16. Both the methods utilize gene expression profiles for drug response prediction. We also considered traditional machine learning methods — random forest (RF) and ElasticNet, which have been used by previous studies for drug response prediction30. As a baseline, we evaluated the performance of RF, ElasticNet and Precily models using expression levels of 500 genes (as opposed to pathway score matrix), selected based on the squared coefficient of variation (CV2). On held out data, Precily based predictions attained the highest correlation with ground truth, closely followed by CaDRReS-Sc. Figure 16, we considered for comparisons, trained drug specific models using off-the-shelf H2O machine learning modules. In this way, one needs to manage one model for each compound. This approach is suboptimal since it does not leverage structural information of the compounds for prediction. For a global picture, we pooled our predictions across drugs and cell lines and obtained a Pearson’s correlation coefficient value of 0.88   for machine learning based prediction. For data preparation, first, for each of the 550 cancer cell lines CCLE, with available drug response data in GDSC, we computed pathway enrichment scores for 1329 canonical pathways from MSigDBove Fig. . We usedter Fig. . For mod16) Fig. .Fig. 1Il10. We reciprocated a similar analysis of CCLE/GDSC on the CCLE/CTRPv2 combination. Notably, only 68 drugs were found common between GDSC2 and CTRPv2 datasets . Gross differences were observed, which prevented us from integrating the two. Precily yielded a Pearson’s correlation coefficient value of 0.84    database features an assorted set of small molecules comprising tool compounds, probes and drugs, including US Food and Drug Administration (FDA)-approved cancer therapeutics16) Fig. , whereas19. While scRNA-seq has been adopted as a method of choice for a large number of clinical studies, we are yet to fully exploit it to predict treatment outcomes at subclonal resolution by factoring in intra-tumor heterogeneity. To demonstrate the potential of Precily in predicting drug response at a single-cell level, we used single-cell datasets from two existing studies. First, we used scRNA-seq data produced by Kinker, G. S. and colleagues for 207 cancer cell lines, of which 116 cell lines overlap with the CCLE dataset31. We re-trained our CCLE/GDSC model such that the set of Kinker, G. S. et al. cell lines was never used in model training. We applied this model to the Kinker, G. S. et al. dataset and achieved a Pearson’s correlation coefficient=0.85   and a population of cells that had gained sensitivity to paclitaxel, after a drug-holiday period. In this study, the metastatic MDA-MB-231 cells were exposed to a paclitaxel drug. Most of the cells died after five days of exposure. However, some of the residual cells, cultured in drug-free medium after withdrawal of the drug, proliferated and established clones. Notably, these cells became more sensitive to paclitaxel on re-exposure. Precily trained on CCLE/GDSC data could correctly predict the in vitro therapeutic response from scRNA-seq data of the paclitaxel sensitive MDA-MB-231 cell population  technologies have refined our appreciation for intra and inter-tumoral heterogeneity across cancer types16) Fig. . Furtherion Fig. .Fig. 2As35. Therefore, there is an unmet need for the selection of optimal drugs for PCa treatment37. We independently validated the CCLE/GDSC trained Precily model on our PCa datasets. We examined the concordance between our in-house gene expression data of PCa cell lines  with the same cell lines from the CCLE dataset and obtained a Pearson correlation in the range of 0.96 to 0.98  for the two biological replicates of LNCaP cells, respectively  to exploit the unique dependence of PCa on androgen signaling for growth and progression. Although ADT is initially effective in most patients, the effect is temporary and cancer cells become resistant with the emergence of castration-resistant prostate cancer (CRPC). To which either taxanes or additional androgen targeted therapies such as enzalutamide are added with modest survival benefits, however, acquired resistance to these drugs eventually emerges. Thus, appropriate drug selection and combination remain crucial in the dynamically evolving landscape of cancer to derive maximum benefit for the patientsely Fig. .Fig. 3An39. This supports the notion that actively proliferating cells are more sensitive to specific anti-cancer drugs, while cells in a cytostatic or quiescent state are more resistant  agonist dihydrotestosterone (DHT) as compared to the vehicle control (VEH) in androgen deprived media conditions; and furthermore, how treatment with the clinically approved AR antagonists bicalutamide (BIC), enzalutamide (ENZ), and apalutamide (APA) under these conditions affected the predicted sensitivity pattern. Predicted sensitivity of a broad spectrum of anti-cancer drugs on treated samples may represent putative altered phenotypic landscapes, indicating therapeutic consequences. In clinical settings, evaluation of post-treatment sensitivity patterns may be crucial for identifying pairing therapies to combat the diverse spectrum of PCa resistance. Overall, LNCaP cells cultured with DHT were predicted to be more sensitive to therapeutic drugs as compared to cells cultured without DHT and AR antagonists Fig. . The celant Fig. . Notablyant Fig. . In contant Fig. . These fant Fig. ..Xenografts are useful in vivo tumor models for directly investigating therapeutic response and predicting anti-cancer drug response in patients with cancer of a similar phenotype. As such, we evaluated our ability to predict drug response in cell line derived xenografts. We used our bulk RNA-seq data from LNCaP xenografts derived from a large and well-annotated PCa progression study investigating responsiveness and subsequent resistance to therapies targeting the AR. LNCaP xenograft tumor establishment and initial growth are dependent on androgens in male mice (PRE-CX). Upon castration, AR activity and tumor growth are suppressed (POST-CX), however, this initial responsiveness to castration reproducibly gives way to castration-resistance (CRPC). Further treatment of CRPC with ENZ initially provides a therapeutic response , however, resistance emerges with time  Fig. . Using CFig. . In contFig. . ENZR tuFig. , with saFig. . While wig. Fig. .Fig. 4Anhttps://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)40 to build a drug response classifier on the basis of tumor bulk RNA-seq data from TCGA. The summary statistics of the dataset used are provided as supplementary data  indicating its independent association with survival. Worth highlighting the sparse availability of data at the resolution of cancer/drug/stage, which makes it difficult to gauge the clinical applicability of our patient model . Group wise contingency tables comparing predicted and ground truth responses are shown in Supplementary Fig. The Cancer Genome Atlas (TCGA) features a large compendium of omic datasets spanning multiple cancer types with gene expression profiles of mostly primary patient tumors and clinical response information. The clinical drug response data includes patient demographics and responses to administered drugs. While sticking to the Precily approach, we aimed to model complete/partial versus non-response in TCGA patients. After filtering and preprocessing, we were left with 3108 patient-drug combinations with recorded clinical responses. These entailed 1443 unique patients (multiple drugs were administered to some patients) and 139 unique drugs, representing 29 cancer types. Given the bewildering diversity of cancer genomes and compound structures, these numbers are clearly inadequate. We, therefore, performed a pooled analysis of the data, agnostic of cancer types/stages. Our formulation of the modeling task allows learning and prediction on virtually any combination of sample-drug pairs. The data matrix prepared for machine learning featured 1427 explanatory variables (1327 pathways plus 100-sized drug descriptor vectors). Due to the data paucity, unlike cell lines, in this case, we used AutoML, the off-the-shelf R library by H2O.ai (41. We predicted the drug response of RAF inhibitor (dabrafenib) and MEK inhibitor (trametinib) in three pre-treatment and matched post-relapse BRAF-mutant RAF/MEK inhibitor-resistant melanoma patients. The journeys of three patients are documented in supplementary data  and RNA-seq. These alterations may be a potential cause of conferring resistance to RAF/MEK inhibitors in these patients41. Notably, our prediction results revealed a similar trend, whereby our probability of response to dabrafenib and trametinib in pre-treatment patient 1 and patient 2 was higher than post-treatment . This is concordant with the original study where this patient was categorized as stable disease based on RECIST criteria.Next, we applied the TCGA data model to an independent dataset for which pre and post-treatment RNA-seq profiles and clinical response information were availableent Fig. . Furtherles Fig. . This isPredicting the drug response of cancer cells is of paramount importance in personalized oncology. In this study, we developed Precily, a deep neural network (DNN) based framework to predict the response to cancer therapy based on gene expression profiles and drug descriptors. Due to the explicit use of pathway enrichment scores, our model highlights the underpinning biological mechanisms contributing to drug resistance. Furthermore, the pathway-based prediction approach allowed us to infer cellular fates upon treatment from single-cell expression data accurately. This can enable drug response prediction at sub-clonal resolution using tumor scRNA-seq data.PTEN negative LNCaP cell line was predicted to have an elevated sensitivity to PI3K/mTOR signaling targeting drugs. Built on pathway scores, Precily allows exploring the association between pathway activities and drug response. This might help identify unexplored signaling pathways as potential therapeutic targets in PCa. Notably, previous studies have broadly suggested that increased PI3K/AKT/mTOR signaling is associated with sensitivity to PI3K inhibitors in LNCaP cell lines and other PTEN null cancer cell lines. Furthermore, mTOR inhibitors might be effective against PTEN null tumors43.We evaluated Precily predictions by showing a reasonable correlation between the experimental LN IC50 for LNCaP cells used in the training data set and the model-predicted values. Further, we noted that the 39. AR antagonists are the primary treatment option for metastatic PCa, which antagonize cellular androgen response pathways on a molecular level. With AR antagonist treatments, LNCaP cells were predicted to have pronounced similarities and differences for ENZ, APA, and BIC treatments, mirroring the complex biological underpinning of the treatment responses. A strong reversal by an AR antagonist of DHT conferred sensitivity was observed for drugs targeting the PI3K/mTOR pathway, with ENZ showing the most profound effect. In contrast, ENZ treatment was predicted to further increase the DHT conferred sensitivity to selected drugs, including cisplatin, docetaxel and paclitaxel, while other AR antagonists may not, e.g. BIC and APA for cisplatin. These predictions suggest that patients on active treatment with ENZ may still benefit from added chemotherapy using cisplatin, while patients on treatment with BIC or APA may not. These predictable differential effects are important considerations for combinatorial therapy to maximize the therapeutic benefit for a given patient while minimizing detrimental side effects of chemotherapy affecting the quality of life.We further explored how drug sensitivity prediction indicated a switch of cellular states into drug-responsive and resistant states in LNCaP cells and xenografts. In the presence of androgens, LNCaP cells were predicted to be more sensitive to cancer therapeutics that target highly proliferative cells. This is expected as androgens drive proliferation in AR-positive PCa cell lines and xenografts44. Further laboratory and preclinical studies into the molecular mechanism behind our predicted sensitivity pattern are needed to confirm these findings.To further evaluate the applicability of the drug sensitivity predictions with Precily, we used data derived from our well-annotated PCa xenograft model, following the progression from early androgen-responsive to CRPC, and then to ENZ treatment responsive and ENZ resistant states. The sensitivity predictions highlight changing vulnerabilities of the tumors in different stages of progression and treatment. Notably, we observed that ENZR tumors are predicted to develop a susceptibility to selected therapeutics providing a new window of opportunity for therapeutic strategies. For example, our model predicted sensitivity to specific drugs targeting the EGFR signaling pathway in case of ENZ treatment, highlighting sapitinib as a potential therapeutic for ENZ resistant patients. While prostate tumors in an androgen replete setting are predicted to be highly resistant to EGFR targeting drugs, a distinct vulnerability is developed during progression to ENZR. As these drugs targeting the EGFR pathway are approved for cancer treatment in other cancer types, this may facilitate the clinical evaluation of combination therapy in PCa patients treated with ENZ or those that have developed ENZR. Previous studies have suggested that a combination of ENZ and EGFR targeting inhibitors might be an effective therapeutic strategy in overcoming ENZ resistanceOur evaluation of the TCGA model using an external BRAF-mutant melanoma dataset resulted in clinically relevant predictions based on tumor RNA-seq samples from the patients. The probability of response for the patients classified in the partial response category was higher than post-treatment due to acquired resistance to the first line of therapy. This suggests that top drugs predicted by our method, for example, cyclophosphamide and cisplatin, might serve as alternative therapies in combination with other drugs to overcome acquired resistance. This warrants further investigation.The major advantage of the discussed framework is that the associated models can be used to infer drug sensitivity for virtually any sample-drug pairs due to the use of numeric drug descriptors. First, this approach enables us to pool cell-drug combinations across cancers, thus providing an opportunity to improve the model performance. Second, the successful prediction by Precily of LNCaP sensitivity to metformin and orlistat confirmed that Precily could be potentially used to evaluate the efficacy of drugs that are not part of the modeling task. Third, Precily based monotherapy sensitivity predictions can provide cues for clinically plausible combination therapies. Thus, Precily can be employed as a first pass screening tool to aid in clinical decisions.A limitation of Precily is that at the level of individual drugs, correlations between observed and predicted IC50 values were suboptimal. However, between drugs, the relative sensitivities are captured reasonably well. We obtained promising results while testing the approach on patient tumor data. However, there is a scarcity of human cancer data with recorded clinical drug responses. With limited data of assorted cancer types from TCGA, we developed and validated a pan-cancer model for drug response prediction, which we believe can be further substantiated with additional data curated from diverse clinical trial studies.To summarize, our current work links bioinformatic predictions of drug response to clinically explainable observations, both in in vitro and in vivo settings. Given only a limited number of clinically relevant molecular subtypes are known in case of PCa, drug response inference based on tumor bulk expression profiles may be leveraged in furthering pharmaco-genomic research.Animal studies were performed with the approval of the University of Queensland and Queensland University of Technology (QUT) Animal Ethics Committees  and in accordance with accepted standards of humane animal care as outlined in the ‘Australian Code of Practice for the Care and Use of Animals for Scientific Purposes’ and the universities’ guidelines for the use of laboratory animals.The human prostate cancer cell lines LNCaP , VCaP , DU145 , PC3  and DUCAP  were cultured in Phenol-red free RPMI medium-1640  supplemented with 5% fetal bovine serum  in a humidified incubator at 37 °C and 5% CO2 and untreated samples were harvested for RNA extraction during their exponential growth phase. Cell lines were authenticated using STR (short tandem repeat) analysis as described in the ANSI Standard (ASN-0002) 2012 by the ATCC Standards Development Organization. Nine short tandem repeat (STR) loci plus the gender determining locus, Amelogenin, were amplified using the commercially available GenePrint®10 System kit from Promega. The cell line samples were processed using the Applied Biosystems® 3500 Genetic Analyzer. Data were analyzed using GeneMapper® v5.0 software (Applied Biosystems). Appropriate positive and negative controls were run and confirmed for each sample submitted. STR profiles of query samples are compared to the ATCC STR Database (or similar) to verify cell line identity. Cell lines with ≥80% match are considered to be related i.e., derived from a common ancestry. For regular mycoplasma testing in cell cultures, the MycoAlert® Mycoplasma Detection Kit , a sensitive luciferase-based biochemical test that detects the activity of mycoplasma enzymes, is used according to manufacturer’s instructions. All cell lines were checked against the list of known misidentified cell lines maintained by the International Cell Line Authentication Committee (ICLAC).For in vitro treatment experiments, LNCaP cells were seeded and cultured for 72 h in a standard medium with 5% FBS. This was followed by a 48 h incubation in androgen-depleted conditions using medium +5% charcoal-stripped serum . Treatment with the androgen targeting drugs (ATTs) enzalutamide  (10 uM), bicalutamide  (10 uM), and apalutamide  (10 uM) was performed for 48 h, either in the absence or presence of 10 nM dihydrotestosterone .45. Calculations of half-maximal inhibitory concentration (IC50) after treatment with the respective drugs were performed with GraphPad Prism. Each data point was performed in triplicate and repeated in at least three independent experiments.LNCaP cells were seeded for 24 h into 96-well tissue culture plates (Corning #COR3599) and treated with serial dilutions of the indicated compounds. Cell viability as a function of metabolic activity was measured by an AlamarBlue (Thermo Fisher Scientific #DAL1025) endpoint assay according to the manufacturer’s instructions (fluorescence measured at 560/590 nm excitation/emission)scid/Arc) were sourced from the ‘Animal Resource Centre’  and underwent a minimum one week acclimatization period upon arrival. Mice were group housed in individually ventilated cages at a 12 h light/dark cycle under specific pathogen-free, temperature- (22–24 degrees Celsius) and humidity-controlled (50–65%) conditions, fed standard chow  and water ad libitum. For the in vivo tumor progression study, xenografts were established by subcutaneous injection of one million LNCaP cells  into the flank of anaesthetised mice . Tumor volume was measured thrice weekly using digital callipers; weight and clinical health score tracked thrice weekly and daily, respectively. Submandibular bleeds were undertaken weekly to collect blood (~100 ul) which was used to track serum PSA (Prostate-Specific Antigen). At a tumor size of ~200 cubic mm (mm3), mice were either surgically castrated (45 mice) or received mock surgery for the PRE-CX group (9 mice) under aseptic conditions, whilst anaesthetised and provided analgesic (carprofen). Tumors from the PRE-CX group (9 mice) were harvested when the ethical tumor size endpoint of 1000 mm3 was reached. Tumors from the POST-CX  group were harvested one-week post-castration at primary serum PSA nadir . CRPC  tumors were harvested when PSA levels recurred to pre-castration levels or when tumor burden reached 1000 mm3 post-castration. For the enzalutamide (ENZ) groups, treatment with 10 mg/kg ENZ  commenced as serum PSA began to rise post-castration. Tumors were harvested either at secondary PSA nadir  while on ENZ treatment, with the remaining tumours collected when PSA had recurred to pre-ENZ treatment levels or when tumor volume reached ethical endpoint of 1000 mm3 despite ENZ treatment . Xenografts were rapidly dissected, snap frozen in liquid nitrogen and stored at −80 °C; until being homogenised in a pre-cooled TissueLyser (Qiagen) for RNA extraction.Five to six-week-old male NOD-SCID mice  according to the manufacturer’s instructions, including DNase treatment. RNA quality and quantity were determined on an Agilent 2100 Bioanalyzer  and Qubit®. 2.0 Fluorometer . Library preparation and sequencing was performed using the Illumina TruSeq Stranded mRNA Sample Prep Kit  with an input of 500 ng − 1 ug total RNA (RIN > 8), followed by paired-end sequencing with a read length of 100–150 bp and yielding about 30–60 M read pairs per sample.46, then trimmed using TrimGalore47, followed by alignments to the human genome (GRCh38 / hg38) and transcriptome  using the STAR48 aligner and read quantification with RSEM49. For xenograft samples, STAR alignment was performed against a chimeric human+mouse reference , followed by RSEM read quantification. Only reads aligned to the human genome were used for downstream analysis. TPM values from the RSEM output were used for GSVA scoring. For predictions, we used averaged GSVA scores of the biological replicates of LNCaP cell line under different treatment conditions.RNAseq raw data was processed through a custom-designed pipeline. Raw reads were assessed with FastQC8 cell lines quantified using the RSEM (RNA-Seq by Expectation-Maximization) software. The corresponding drug response information for the cell lines was sourced from the GDSC2 dataset of the Genomics of Drug Sensitivity in Cancer (GDSC) database9. In the GDSC2 dataset, some drug-cell line pairs have multiple LN IC50 measurements. In such cases, we averaged the LN IC50 values. The RNA-seq gene expression profiles of 550 CCLE cell lines overlapping with the GDSC2 dataset cell lines were used for the model training. This matrix contained 57820 Ensembl Gene IDs, which were converted into official gene symbols using gencode.v19.genes.v7_model.patched_contigs.gtf annotation file. This resulted in multiple Ensembl gene IDs corresponding to some of the individual gene symbols. We considered the average expression in such cases. At this stage, our expression matrix comprised 54301 genes and 550 cell lines. This matrix was subjected to log2 transformation with the addition of a pseudo count of 1.For machine learning based modeling of drug response measured by half-maximal inhibitory concentration (LN IC50), we used publicly available TPM (transcript per million) normalized RNA-seq gene expression profiles of 1019 Cancer Cell Line Encyclopedia (CCLE)50 encompassing 33 tumor types. We used Illumina HiSeq RNA-seq v2 data processed at the gene level using RSEM49. The clinical drug response information for the patient samples was fetched from the NCI Genomic Data Commons portal51. Drug names and response information was collected from clinical metadata and rectified for typographical and spelling errors and to harmonize commercial names and molecular drug names. We categorized complete response and partial response patients as responders. Patients with clinically progressive and stable diseases were marked as non-responders. RNA-seq gene expression profiles of cancer types with clinical response data for fewer than two patients were excluded. At this stage, the filtered data contained gene expression profiles for 29 cancer types. Then for individual cancer types, scaled estimates from gene-level RSEM files were transformed into TPM by multiplying with a factor of 1e6 followed by log2 transformation with the addition of pseudo count 1. For the patients with identical TCGA barcodes, we averaged their gene expression profiles for downstream analysis.Analogous to cell lines, on tumor mRNA sequencing data from TCGA, we modeled drug-response in terms of responder and non-responder. TCGA RNA-seq data were downloaded from the Broad GDAC firehose26. However, SMILES were not available for all the molecular compounds. As a result, we ended up with SMILES of 173 and 139 compounds for 550 CCLE cell lines and 1443 unique TCGA patients, respectively. The SMILESVec python tool was used to convert these SMILES into vector embeddings by utilizing data of embeddings trained on Pubchem and embedding of size 100 25.We obtained drug response information for 192 compounds from the GDSC2 dataset for the 550 cell lines in the CCLE dataset and clinical response information for 215 compounds for 1517 TCGA patients. The chemical structure information for these molecular compounds was retrieved in terms of a simplified molecular-input line-entry system (SMILES) using PubChemPy17 R software package to compute GSVA scores based on the log2(TPM  +  1) gene expression matrix for selected gene sets from Molecular Signatures Database (MSigDB)24 with min.sz set as 5. We used the c2 collection of canonical pathways (MSigDB.CP.v.6.1) consisting of 1329 gene sets. We integrated the pathway score matrix with the vector embeddings of the drug features. Our final CCLE cell line training dataset constituted 80056 cell line-drug combinations in rows and 1429 features entailing 1329 pathways and drug features of vector size 100 for each molecular compound as the explanatory variable and LN IC50 as the response variable in columns. For TCGA patient data, gene expression profiles of individual cancer types were transformed into pathway scores. The GSVA scores of the samples where drug response information was available in each cancer category were merged based on common pathways. The final matrix consisted of 3108 patient-drug combinations and, 1427 features (pathways & drug descriptors) and the response variable .To train models, we used pathway activity scores. We used the Gene Set Variation Analysis (GSVA)52. We performed a grid search on each fold of the training dataset. For every fold, the mtry and number of trees were varied from 1 to 10 (with the step size of 1) and 100 to 1000 (with the step size of 100), respectively. We selected the five best models with minimum Mean Squared Error (MSE) for each training data subset. Finally, we trained five models (based on pre-learned hyperparameters) on the entire training data using the parsnip R package53. ElasticNet was implemented using the caret54 and glmnet55 R packages. For each of the five training folds, the caret, by default, performs bootstrapping 25 times to find out the optimal model based on the minimum value of Root-Mean-Square Error (RMSE). These five optimal models were retrained on the entire training dataset.We formulated the drug response prediction task with genes/pathway enrichment scores, in tandem with drug descriptors as a regression problem. We utilized various machine learning techniques for response prediction. We split the CCLE training dataset into a 90% training set (72262 cell line-drug pairs) and a 10% test set (7794 cell line-drug pairs) such that there was no overlap in the cell lines. We employed k-fold cross-validation for hyperparameter tuning and divided the training set into 5 non-overlapping folds. Our five validation sets comprised 14,761, 14,302, 14,769, 14,212 and 14,218 cell line-drug pairs, respectively. The number of features — 1429 (pathway enrichment scores and drug descriptors) were the same in training, validation and test datasets. We used Random Forest implementation from the R package ranger56 to find the optimal set of parameters for our deep learning models. We employed Hyperband57 with 5-folds cross-validation on training data to find the best hyper-parameters based on validation loss. We tried the following hyper-parameters: the number of layers varied between 2 and 6, and the number of neurons, between 128 and 256 (with a step size of 4). The number of epochs was set to 30. Dropout layers were added between the layers to avoid the overfitting of the models. We tuned for drop-out rates . We used the ADAM optimizer with different learning rates . We optimized Mean Squared Error (MSE) as the loss function. Finally, five models were trained on the entire dataset with fold-specific tuned hyper-parameters for 50 epochs with a batch size of 128. These models were used for reporting performance on independent test data.A deep neural network (DNN) was trained using the Keras framework. The DNN architecture comprised one input layer entailing all features (pathway enrichment scores and drug descriptors) in the dataset, followed by one hidden layer of size 512, with Rectified Linear Unit (RELU) as an activation function. We kept the first two layers fixed. We used the Keras Tuner libraryFor the model using genes as features (instead of pathways), the only difference in the architecture was the size of the input layer, which was set to 600 (500 top highly variable genes and drug descriptors of size 100). Of note, ranger and ElasticNet were run with the same strategies discussed above.21 and another by Sakellaropoulos, Theodore, et al.16. CaDRReS-Sc is a machine learning framework that incorporates a matrix factorization-based recommender system for drug response prediction. We applied CaDRReS-Sc to the CCLE gene expression dataset and the corresponding drug response information from the GDSC database. CaDRReS-Sc was run with default settings. For the Sakellaropoulos, Theodore, et al. method, we trained models for individual drugs using the author recommended parameters, except for the variance cut-off (varcut) parameter, where we used a value of 10, to avoid late convergence. This was important because, under this strategy, we had to train drug specific models. All methods were evaluated on the same data. Final performance has been reported in terms of Pearson’s correlation for predicted vs observed IC50 value for individual drugs.We benchmarked Precily against previously published methods – CaDRReS-Sc by Suphavilai, Chayaporn, et al.10. For all these compounds, cell line specific IC50 values were obtained from the PharmacoGx R package58. We discarded IC50 outliers using the interquartile range (IQR) rule. At this stage, our data constituted 153899 drug-cell line combinations in rows and 1429 features, including pathway and drug features and LN IC50 as the response variable in columns. The remaining steps involved in evaluating the performance of Precily were kept identical as in the case of CCLE/GDSC analysis. In this analysis, 90% training dataset consisted of 138254 cell line-drug pairs and 1429 features comprising pathway enrichment scores and drug descriptors and a 10% test set (15645 cell line-drug pairs and 1429 features comprising pathway enrichment scores and drug descriptors). The five validation sets comprised 27682, 27674, 27982, 27565 and 27351 cell line-drug pairs, respectively. 1429 features were common across all five sets (pathway scores and drug descriptors), respectively.CTRPv2 features a sensitivity of various well established cancer cell lines to an assorted set of small molecules comprising tool compounds, probes and drugs, including US Food and Drug Administration (FDA)-approved cancer therapeutics. We obtained SMILES for 377 compounds reported in CTRPv240 framework in R for training the TCGA dataset. We split this dataset in the ratio of 90% for training and 10% for testing. This 90% training dataset was subjected to the h2o.automl function with five-fold cross-validation, ensuring there was no overlap of patients across folds and a maximum number of models was specified as 20. This resulted in automated training of 34 machine learning models , including Deep learning, DRF, GBM, GLM, XGboost, XRT, and stacked ensemble models. Deep learning offered suboptimal performance  and Pearson correlation (ρ). R2 was calculated using the R caret54 package. For the TCGA dataset, we computed AUC, AUC-PR and F1 scores.We used two accuracy metrics to measure the performance of our models for the regression task: the coefficient of determination  from the same study. At this stage, we were left with 53299 cells spanning 198 cell lines. This matrix was transformed into TPM by scaling with a factor of 1e6 and dividing the UMI count of genes by the total UMI count of the sample. The UMI counts are independent of gene length bias61. We log2-transformed this normalized matrix with the addition of a pseudo count of 1. The gene expression values of the same cell lines were averaged. This matrix was converted into pathway scores using GSVA with default parameter settings. Our final validation set consisted of 17279 drug cell line combinations entailing 116 cell lines that overlapped with the cell lines featured in the GDSC2 dataset and tested against 173 drugs.The scRNA-seq cell line pre-QC UMI count dataset comprising 30314 genes and 56982 cells encompassing 207 cell lines was obtained from Kinker, G. S. et al study48 with GRCh37 reference genome and GRCh37 GTF file from Ensembl (release 75)62. To estimate gene expression, we used HTSeq-count63. The HTSeq generated read counts were transformed into TPM values by dividing raw counts by gene length, then scaling with a factor of 1e6 and dividing by total read counts in the sample. We discarded the genes for which gene length was not retrieved by the EDASeq R package64. This was followed by the conversion of Ensembl gene IDs to official gene symbols using Homo_sapiens.GRCh37.75.gtf annotation file. We retained genes having a TPM of at least 1 in at least 10% of samples. This matrix was subjected to log2 transformation with the addition of pseudo count 1. We averaged gene expression values of five samples each of naive, stressed, and cells more sensitive to paclitaxel, respectively and converted them into pathway scores using GSVA. We predicted drug response for treatment-naive and population of cells more sensitive to paclitaxel in terms of LN IC50 values.Lee et al. dataset was obtained by processing FASTQ files of MDA-MB-231 cells from SRA id SRP040309. We align FASTQ files using STAR aligner41) also included therapy response information. For our analysis, we transformed RPKM normalized RNA-seq profiles to TPM by dividing each RPKM value by the sum of the RPKM values for all genes in the sample and multiplying by one million65. The TPM normalized matrix was log2 transformed with the addition of a pseudo count of 1. The resulting matrix was transformed into pathway scores using GSVA. For the three patients for whom information on the mechanism of acquired resistance to dabrafenib/trametinib was available, we predicted a therapeutic response to dabrafenib and trametinib.We used publicly available Reads Per Kilobase of transcript per million mapped reads (RPKM) RNA-seq profiles of BRAF mutant melanoma patients for drug response prediction. This dataset was comprised of six paired patients for which RNA-seq was performed before and after treatment with RAF or RAF + MEK inhibitors during disease progression. This dataset  and in R with ggplot2 (3.3.5), pheatmap (1.0.12), ggpubr (0.4.0) and ggridges (0.5.3).Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesDataset 1Dataset 2Reporting Summary


Treatment of prostate cancer (PCa) has changed considerably in the last decade due to the introduction of novel androgen receptor (AR)-targeted agents (ARTAs) for patients progressing on androgen deprivation therapy (ADT). Preclinical research however still relies heavily on AR-negative cell line models. In order to investigate potential differences in castration-resistant PCa (CRPC) growth, we set out to create a comprehensive panel of ARTA-progressive models from 4 androgen-responsive AR wild-type PCa cell lines and analyzed its androgen response as opposed to its ADT-progressive counterparts. Parallel cultures of VCaP, DuCaP, PC346C, and LAPC4 were established in their respective culture media with steroid-stripped fetal calf serum (FCS) [dextran-coated charcoal-stripped FCS (DCC)] without androgen (ADT) or in DCC plus 1 μM of the ARTAs bicalutamide, OH-flutamide, or RD162 . Cell growth was monitored and compared to those of parental cell lines. Short-term androgen response was measured using cell proliferation 3--2,5-diphenyltetrazolium bromide (MTT) assay. qRT-PCR was performed to assess the mRNA expression of markers for AR signaling, steroidogenesis, glucocorticoid receptor (GR) signaling, epithelial-mesenchymal transition (EMT), and WNT signaling. Out of 35 parallel cultures per cell line, a total of 24, 15, 34, and 16 CRPC sublines emerged for VCaP, DuCaP, PC346C, and LAPC4, respectively. The addition of bicalutamide or OH-flutamide significantly increased CRPC growth compared to ADT or RD162. VCaP, DuCaP, and PC346C CRPC clones retained an AR-responsive phenotype. The expression of AR and subsequent androgen response were completely lost in all LAPC4 CRPC lines. Markers for EMT and WNT signaling were found to be elevated in the resilient PC346C model and CRPC derivatives of VCaP, DuCaP, and LAPC4. Although the resistant phenotype is pluriform between models, it seems consistent within models, regardless of type of ARTA. These data suggest that the progression to and the phenotype of the CRPC state might already be determined early in carcinogenesis. Due to the dependency of normal prostate  and prosPersisting AR signaling has long since been recognized as a resistance mechanism to ADT. However, maximal androgen blockade (MAB) by addition of the antiandrogens bicalutamide (BIC)  or OH-flvia direct antagonism , a phenomenon that remains rare in CRPC. Traditional HSPC models like LNCaP, MDA PCa2, VCaP, DuCaP, and LAPC4 are derived from PCa metastases after hormone treatment. Nevertheless, these cell lines need androgens for their optimal growth and survival and therefore are considered androgen-responsive  of CRPC clones from 4 distinct AR wt PCa cell lines  by growing them in androgen-depleted medium alone (ADT) or with ARTA BIC, FLU, or RD162, an antiandrogen with a similar molecular structure and antiandrogenic efficacy as enzalutamide and apalutamide (PC346C p25) was generated in our lab as described previously  was gene and mainThe ADT condition consisted of each parental cell line’s respective medium, substituting FCS with dextran-coated charcoal-stripped FCS (DCC) and removal of R1881. For ARTA conditions, BIC (kindly provided by Organon), FLU (Shering Plough Research Institute), or RD162  was added to the medium at a final concentration of 1 μM. Cell culture medium was refreshed twice per week, and at 80%, confluency cells were trypsinized and passaged. Once time between passaging approximated that of parental cell lines grown in standard androgen-supplemented medium, clones were considered resistant.-11, 10-10, and 10-9 M R1881 for VCaP, DuCaP, and PC346C as androgen concentration range for proliferation assays. For LAPC4, we selected 0, 10-10, 10-9, and 10-8 M. After a 9-day incubation period at 37°C and 5% O2, cell numbers were estimated using the 3--2,5-diphenyltetrazolium bromide (MTT) assay as described previously  and dimethyl sulfoxide (DMSO) (for ARTA) as vehicle controls. Based on preliminary androgen response curves, we selected 0, 10eviously .2 transformed, clustered using Cluster (version 3.0)   using coion 3.0) .For qPCR, cells harvested at passaging were stored as cell pellets at -80°C until further processing. RNA was isolated using RNA-Bee . Reverse transcriptase and qPCR runs were performed as described previously  using anRelative gene expression levels were expressed as ΔCt relative to the geometric mean of housekeeping genes GAPD and PBGD. GraphPad Prism (version 9) was used to group and visualize -ΔCt values. For cluster analyses, -ΔCt values were median centered per gene and clustered using Cluster using complete linkage with Spearman Rank Correlation (version 3.0) and visualized by Java TreeView (version 1.16r4). For statistical analyses and clustering of samples, undetectable gene expression (Ct >40) was normalized to a –ΔCt of -20.2 test was used to test the null hypothesis that overall take rates between cell lines, between ADT and ARTA, and between ADT and ARTA within each cell line were equal. For the proliferation assay analysis, a regular two-way ANOVA was used to test the null hypothesis that fold growth rates between parental and CRPC cells and between hormonal/ARTA conditions were equal. For comparison of mRNA expression levels between cell lines and culture conditions, the ΔCt values were used as input to maintain a normal distribution on a log2 scale, using a regular one-way ANOVA with a Tukey-corrected post-hoc test to test the null hypothesis that these were equal between parental cell lines and/or ADT and specified ARTA. For all tests, a p < 0.05 was considered statistically significant. To assess at what time CRPC cell growth had stabilized, 5-knot smooth splines were fitted through passage times, defined as time in days until next passage, on the y-axis and passage number on the x-axis using GraphPad Prism (version 9).All statistical analyses were carried out using SPSS version 26 . The χPCa cell lines VCaP, DuCaP, PC346C, and LAPC4 were long-term cultured in 5–10 independent replicates in androgen-depleted medium alone (ADT) or with ARTA BIC, FLU, or RD162. The emergence of CRPC sublines  alone (p < 0.0001). However, this effect was cell line-dependent, with PC346C showing no preference for any selective pressure, while VCaP, DuCaP, and LAPC4 showed enhanced CRPC survival in the presence of BIC or flutamide compared to either ADT or the more potent AR antagonist RD162 (The median passage number (range) for successful establishment of CRPC sublines with similar growth rates as the parental line was 21 14–41) for VCaP, 24 (21–27) for DuCaP, 27 (23–47) for PC346C, and 20 (18–27) for LAPC4  and inhibited at 10-9 M R1881, with further growth induction with AR antagonists at 1 µM at 10-10 R1881 and up, implying hypersensitivity to the AR stimulus. Parental and CRPC DuCaP sublines showed growth induction with 1 µM ARTA with an optimal growth stimulus at 10-11 M. Interestingly, higher levels of R1881 (10-9 M) that were growth stimulatory for parental DuCaP showed growth inhibition in the CRPC clones. This demonstrates an increased AR hypersensitivity in CRPC sublines. In PC346C, androgens continued to induce cell growth in CRPC sublines with a similar optimum concentration of 10-10 M R1881 as observed in the parental lines. Incubation with ARTA reduced androgen-induced cell growth both in parental and CRPC sublines. However, the increased basal proliferation of CRPC sublines  in castrate conditions was virtually unaffected by ARTA, implying that castration-resistant growth was independent of AR.Apart from all CRPC sublines showing higher proliferation rates in castrate conditions, we found marked differences in AR response for CRPC sublines from each model. The growth of VCaP CRPC sublines was stimulated at lower levels of R1881 compared to parental cells  or ARTA selection  was primarily defined by the cell line origin. PC346C was most successful with the establishment of CRPC sublines in all attempts but one. This parallels our previous reports of PC346C also acquiring resistance to abiraterone, enzalutamide, and docetaxel , 46. In via random mutations due to epigenetic changes and chromosomal instability  antiandrogens leading to increased expression of variants and steroidogenic enzymes and ultimately resistance. It is likely that the higher antiandrogenic potency of RD162 over BIC , 16 bloc kinases . This ARa priori dependence on canonical AR signaling, which is further enhanced by selection with ADT and ARTA.Parental PC346C and LAPC4 demonstrated the lowest levels of AR and enzymes for the steroid synthesis of CYP17A1 and AKR1C3. In both models, the CRPC sublines all showed enhanced AR-independent growth and unchanged to undetectable levels of enzymes for steroidogenesis. This suggests little to minimal in vitro that was unaffected by RD162. This may indicate that enhanced PSA expression may well be regardless of GR status. Since the AR was suggested to be a negative transcription regulator of GR, further studies are needed to define if the GR is an active driver of resistance or a bystander effect of altered AR dependency.GR expression has been reported as a driver of enzalutamide resistance in LNCaP xenografts  and alsoPC346C—the most resilient model in terms of ability to form CRPC sublines relative to the other three—was the only cell line that had detectable and substantial expression of SNAI1 and WNT5A at baseline, with elevated expression levels in CRPC derivatives. While undetectable at baseline, SNAI1 expression became detectable in VCaP and DuCaP at progression to CRPC. In LAPC4, CRPC sublines expressed both ZEB-1 and SNAI1, while also WNT7B expression was enhanced. This warrants further interrogation of EMT and WNT signaling markers in the HSPC population if these may predict a subsequent treatment response.Taken together, an important observation can be made: in our well-defined setup, the potency to become castration-resistant and the mechanism of castration resistance seem to be already defined before full resistance has occurred. If true for complex PCa biology in patients, response to hormone therapy could be comprehended up front and may identify patients who need aggressive treatment early on based on biological behavior rather than the currently surrogate measure of aggressiveness: extent of disease at diagnosis , 56.The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.JM, WW, and GJ contributed to conception and design of the study. JM, WT, SE, AJ-A, ND, and AR performed the experiments and collected the data. SE, AJ-A, ND, and AR organized the database. JM performed the statistical analysis. JM, WW, and GJ wrote the article. All authors contributed to article revision and read and approved the submitted version.This project was supported by TI-Pharma grant T3-107.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


SC-enantiomer of dimethyl α-hydroxy-benzylphosphonate, and the optical purity was retained. A part of the acyloxyphosphonates was tested against eight tumor cell lines of different tissue origin at c = 50 μM concentration. The compounds elicited moderate cytostatic effect against breast, skin, prostate, colon, and lung carcinomas; a melanoma cell line; and against Kaposi’s sarcoma cell lines. Then, dose-dependent cytotoxicity was assayed, and benzoylation of the α-hydroxy group was identified as a moiety that increases anticancer cytotoxicity across all cell lines. Surprisingly, a few analogues were more toxic to multidrug resistant cancer cell lines, thus evading P-glycoprotein mediated drug extrusion.An efficient method applying acyl chlorides as reagents was developed for the acylation of the hindered hydroxy group of dialkyl α-hydroxy-benzylphosphonates. The procedure did not require any catalyst. A few acylations were also performed with the  A simil4c*: 31P NMR (CDCl3) δ 20.15; δPracemic (CDCl3) 18.0 +found = 281.0553, calculated: 281.0555, C11H15O5PNa; [α]D25 = −43.3 .l3) 18.0 ; [M + H]4b*: 31P NMR (CDCl3) δ 20.18; [M + H]+ = 273, [M + Na]+found = 295.0712, calculated: 295.0711, C12H17O5PNa; [α]D25 = −44.0 .4d*: 31P NMR (CDCl3) δ 20.23; δPracemic (CDCl3) 20.1 +found = 343.0711, calculated: 343.0711, C16H17O5PNa; [α]D25 = +26.2 .l3) 20.1 ; [M + H]2. The cells were grown to confluency and then divided into 96-well tissue culture plates  with the initial cell number of 5.0 × 103 cells/well. After 24 h incubation at 37 °C, the cells were treated with the compounds in 200 μL final volume containing 1.0 v/v% DMSO  at 50 μM concentration overnight, whereas control cells were treated with serum-free medium only, or with DMSO (c = 1.0 v/v%) at the same conditions. After incubation, the cells were washed twice with serum-free medium. Subsequently, the cells were cultured for additional 72 h in 10% serum containing medium at 37 °C; then, the MTT  solution  was added to each well. The respiratory chain [620 values were subtracted from OD540 values. The percent of cytostasis was calculated with the following equation:treated and ODcontrol correspond to the optical densities of the treated and the control wells, respectively. In each case, two independent experiments were carried out with four parallel measurements. Statistical analysis of data was performed using Student’s t test at the 95% confidence level.In vitro cytostatic effect of the compounds was studied on MDA-MB-231 human breast adenocarcinoma , A431 hury chain  and othery chain  reduce Mry chain . The amory chain . After 3The 143/B osteosarcoma cell line was a kind gift of Dr. József Balla . Mes-Sa and Mes-Sa/Dx5 uterine sarcoma cell lines were obtained from ATCC  in 2012, where they were characterized by DNA fingerprinting. A431 was obtained from ATCC as well. We purchased CAKI-1, HCT116, MDA-MB-231, and OVCAR-8 from NIH NCI from the NCI-60 cell line panel. 143/B cells were maintained in MEM; A431, Mes-Sa, and Mes-Sa/Dx5 cell lines were maintained in DMEM. NCI-60 panel cell lines were cultivated in RPMI. Media were supplemented with 10% FBS, 5 mmol/L glutamine, and 50 units/mL penicillin and streptomycin. Mes-Sa cells were engineered by lentiviral transduction to stably express the fluorescent protein mCherry, while Mes-Sa/Dx5 cells express eGFP (see more in reference ). After A total 2500 cells/well were seeded in 20 µL of the 143/B, A431, and NCI-60 panel cell lines on 384-well plates. Cells settled for 24 h, when serial dilution of the drugs were added in an additional 40 µL. After 72 h incubation, we added PrestoBlue cell viability reagent  for an hour in a final volume of 5%. Fluorescent intensity of the reagent was read by a PerkinElmer EnSpire microplate reader  at 555 nm excitation and 585 nm emission wavelengths. pIC50 values were calculated by sigmoidal curve fitting by our custom program, written by Judit Sessler in C#.The fluorescent Mes-Sa mCherry and Mes-Sa/Dx5 eGFP cell lines were seeded and cocultured on 384-well plates with a density of 2 × 1250 cells/well. Cells settled for 24 h when serial dilutions of the drugs were added in an additional 40 µL (with or without tariquidar). After 144 h incubation, the fluorescent intensity of mCherry  and eGFP  was measured. pIC50 values were calculated by our custom program. Liquid handling was performed by a Hamilton StarLet robot.Sc-enantiomer, the optical purity was preserved. To evaluate structure–activity relations, the hydroxyphosphonate derivatives were first tested for cytostatic activity at a low concentration range utilizing eight different cell lines. Their activity was cell- and concentration-dependent. Based on the SAR observed, substitution of hydrogens in the phenyl ring, preferably with trifluoromethyl-group, and more preferably in the meta- and ortho-positions [In summary, a series of hydroxyphosphonates was converted to the corresponding acyloxyphosphonates by the catalyst-free reaction with different acyl chlorides. During the acylation of the ositions , could i


Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a mitochondrial 1‐carbon metabolism enzyme, which is an attractive anticancer drug target as it is highly upregulated in cancer but is not expressed in healthy adult cells. Selective MTHFD2 inhibitors could therefore offer reduced side‐effects during treatment, which are common with antifolate drugs that target other 1C‐metabolism enzymes. This task is challenging however, as MTHFD2 shares high sequence identity with the constitutively expressed isozymes cytosolic MTHFD1 and mitochondrial MTHFD2L. In fact, one of the most potent MTHFD2 inhibitors reported to date, TH7299, is actually more active against MTHFD1 and MTHFD2L. While structures of MTHFD2 and MTHFD1 exist, no MTHFD2L structures are available. We determined the first structure of MTHFD2L and its complex with TH7299, which reveals the structural basis for its highly potent MTHFD2L inhibition. Detailed analysis of the MTHFD2L structure presented here clearly highlights the challenges associated with developing truly isoform‐selective MTHFD2 inhibitors. Pinning down selectivity: MTHFD2 is an anti‐cancer drug target only expressed in cancer cells. However, MTHFD2 inhibitors also target MTHFD1 and MTHFD2L, which are expressed in healthy cells. The first structure of human MTHFD2L and its complex with a potent inhibitor reveals the structural basis for inhibition differences between isoforms and highlights the challenges and opportunities in developing selective MTHFD2 inhibitors targeting the folate, cofactor and allosteric binding sites. In eukaryotes 1C‐metabolism is highly compartmentalized between the cytoplasm and the mitochondria in the cell, vs. response ‐ Variable slope (four parameters) to the data using GraphPad Prism 6.0 software .MTHFD2L activity was monitored using the NAD(P) Glo™ Detection System (Promega). The assay monitors the concentration of NADPH and in the presence of this reduced cofactor the enzyme Reductase reduces a proluciferin reductase substrate to form luciferin, which can then be quantified using Ultra‐Glo recombinant Luciferase. The luminescence produced is proportional to the amount of NADPH in the sample. The assays were performed according to the manufacturers instructions. Each reaction consisted of assay buffer , a not‐for‐profit charitable foundation. Thomas Helleday is a board member of THF. The remaining authors declare no competing interests.1


BLM gene, which encodes the BLM helicase, acting in DNA replication and repair processes. Here, we describe the gene expression profiles of three BS fibroblast cell lines harboring causative, biallelic truncating mutations obtained by single-cell (sc) transcriptome analysis. We compared the scRNA transcription profiles from three BS patient cell lines to two age-matched wild-type controls and observed specific deregulation of gene sets related to the molecular processes characteristically affected in BS, such as mitosis, chromosome segregation, cell cycle regulation and genomic instability. We also found specific upregulation of genes of the Fanconi anemia pathway, in particular FANCM, FANCD2 and FANCI, which encode known interaction partners of BLM. The significant deregulation of genes associated with inherited forms of primary microcephaly observed in our study might explain in part the molecular pathogenesis of microcephaly in BS, one of the main clinical characteristics in patients. Finally, our data provide first evidence of a novel link between BLM dysfunction and transcriptional changes in condensin complex I and II genes. Overall, our study provides novel insights into gene expression profiles in BS on an sc level, linking specific genes and pathways to BLM dysfunction.Bloom syndrome (BS) is an autosomal recessive disease clinically characterized by primary microcephaly, growth deficiency, immunodeficiency and predisposition to cancer. It is mainly caused by biallelic loss-of-function mutations in the  BLM including nonsense and missense variants as well as frameshifting indels cause the BS phenotype  is a rare genetic disease that was first described in 1954 in children with growth retardation and sunlight sensitivity . A narrorocesses . Furtherrocesses . Recentlhenotype .et al.  control fibroblast cell lines (GM08398 and GM00409) and three BS patient fibroblast cell lines  were used for scRNA profiling. The causative mutations were confirmed using Sanger sequencing on DNA isolated from each cell line . For sinTo determine the differences in transcriptional profiles between the BS patient cells and WT cells, we performed scRNAseq on three BS patient fibroblast cell lines and two WT fibroblast cell lines . QualityBLM gene   to detered terms . In addiMoreover, we observed that all of the top 20 terms of the MeSH-based analysis were cancer-related terms and included cancer predisposition syndromes such as Fanconi anemia (FA) and Li-Fraumeni syndrome ,28. One BLM expression does not significantly affect the expression levels of the other members of the BTR complex, namely, TOP3A, RMI1 and RMI2. Still, we can currently not exclude an effect of BLM deletion on protein stability of BTR complex members . As shown in ed genes , signified genes . In addi as well ,44.Technical advances in sc transcriptomics offer the opportunity to determine in more detail changes in transcriptional signatures in human disease and, thereby, to gain deeper insights into the underlying disease-causing mechanisms. In this study, we systematically analyzed transcription signatures and molecular pathways in sc RNA sequencing in primary dermal fibroblasts from three patients with BS. The performance of the ICELL8  platformFANCD2 has a well-described function during DNA repair as well as an important role in replication fork management and stabilization, which implies its independent roles from the rest of the FA-related genes and proteins. Also, FANCD2 has been shown to localize to ultrafine DNA bridges, which occur frequently in BS patient cells because of the high rate of mutations and resulting replication stress  is a rare disorder mainly characterized by severe microcephaly at birth and mental retardation of variable degree in the absence of any additional significant neurological findings, malformations or growth anomalies . MCPH issyndrome . CDK5RAPentation ,59. Preventation . The anaor cells ,61. OverSMC2 and SMC4 as well as other complex subunits . Condensin complexes are essential for the structural organization of mitotic and meiotic chromosomes. Studies have shown that chromosome assembly is impaired when condensin subunits are dysfunctional or depleted. Cells attempt to undergo anaphase but chromosomes frequently fail to segregate, leading to lagging chromosomes and the formation of chromatin bridges  and diluted in 1× DPBS before further steps.Two WT control fibroblast cell lines (GM08398 and GM00409) and three BS patient fibroblast cell lines  were purchased from Coriell Institute for Medical Research (see Genomic DNA was extracted from one WT control and three patient cell lines using DNeasy Blood and Tissue kit (Qiagen). Specific primers were designed to cover ~500 base pairs around the mutations and the regions were PCR-amplified using Qiagen Multiplex PCR kit (Qiagen). The PCR products were sequenced with corresponding primers using canonical Sanger sequencing at SeqLab-Microsynth .Resuspended cells were stained with Hoechst 33342 and Propidium Iodide  in accordance with the manufacturer’s instructions. After microscope check for proper staining, the cells were counted using CASY cell counter and serial dilutions were prepared to obtain 1 cell/50 nl concentration before sc sorting. Single cells were dispensed to a 5184-well microchip using ICELL8 (Takara Bio) system  and the First, the resulting fastq file was demultiplexed using the demux script of the CogentAP analysis pipeline version 1.0 . The single-end 51 base-pair reads were analyzed by using the analysis part from the CogentAP pipeline. This analysis utilized cutadapt  version version (2.0.1) The R-package Limma + Voom  for the Dermal fibroblast cells were lysed in solution containing 20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA (pH 8), 1% NP40 and 2.5% SDS supplemented with protease inhibitors . Protein samples were loaded to a 4–20% Mini-PROTEAN TGX stain-free gel (Bio-Rad), transferred to a PVDF membrane and incubated with respective primary antibodies. The antibodies and the dilutions used in this study were the following: anti-SMC2 , 1:2000; anti-SMC4 , anti-ß-Actin , 1:1000. After incubating the membrane with horseradish peroxidase-conjugated secondary antibodies, the proteins were visualized with enhanced chemiluminescence detection  method.HMG-2021-CE-00720_Suppl_Fig_S1_ddab373Click here for additional data file.HMG-2021-CE-00720_Suppl_Fig_S2_ddab373Click here for additional data file.HMG-2021-CE-00720_Suppl_Fig_S3_ddab373Click here for additional data file.HMG-2021-CE-00720_Suppl_Fig_S4_ddab373Click here for additional data file.HMG-2021-CE-00720_Suppl_Fig_S5_ddab373Click here for additional data file.HMG-2021-CE-00720_Supplementary_Table1_ddab373Click here for additional data file.HMG-2021-CE-00720_Supplementary_Table2_ddab373Click here for additional data file.HMG-2021-CE-00720_Supplementary_Table3_ddab373Click here for additional data file.HMG-2021-CE-00720_Supplementary_text_final_ddab373Click here for additional data file.