Hepatitis C virus (HCV) genotype 4 is highly prevalent in the Middle East and parts of Africa. Subtype 4d has recently spread among high-risk groups in Europe. However, 4d infectious culture systems are not available, hampering studies of drugs, as well as neutralizing antibodies relevant for HCV vaccine development. We determined the consensus 4d sequence from a chronic hepatitis C patient by next-generation sequencing, generated a full-length clone thereof (pDH13), and demonstrated that pDH13 RNA-transcripts were viable in the human-liver chimeric mouse model, but not in Huh7.5 cells. However, a JFH1-based DH13 Core-NS5A 4d clone encoding A1671S, T1785V, and D2411G was viable in Huh7.5 cells, with efficient growth after inclusion of 10 additional substitutions [4d(C5A)-13m]. The efficacies of NS3/4A protease- and NS5A- inhibitors against genotypes 4a and 4d were similar, except for ledipasvir, which is less potent against 4d. Compared to 4a, the 4d(C5A)-13m virus was more sensitive to neutralizing monoclonal antibodies AR3A and AR5A, as well as 4a and 4d patient plasma antibodies. In conclusion, we developed the first genotype 4d infectious culture system enabling DAA efficacy testing and antibody neutralization assessment critical to optimization of DAA treatments in the clinic and for vaccine design to combat the HCV epidemic. Hepatitis C virus (HCV) infection is a major cause of chronic hepatitis, which eventually can lead to liver cirrhosis and hepatocellular carcinoma (HCC). The virus contains a positive-stranded RNA genome that consists of an open-reading frame (ORF) flanked by 5′-and 3′-untranslated regions (UTRs), critical for viral replication . The ORFCombinations of direct-acting antivirals (DAAs), targeting NS5A and the NS3/4A protease (PI), or NS5A and the NS5B polymerase , have reAlthough DAAs are highly effective therapies, these might not necessarily prevent re-infections, which could hamper the elimination of HCV. In addition, protective immunity would be an important factor for protection against new infections. Worldwide, about 2 million acute infections have been reported annually, and a vaccine would therefore be an important tool for achieving the WHO goal of eliminating HCV as a public health threat. One of the key objectives after vaccination is to induce neutralizing antibodies (NAbs) to gain protective immunity . FurtherGiven the advantages of infectious HCV cell culture systems in studies of antivirals and for vaccine development, there have been tremendous efforts to develop such systems. However, it remains a great challenge to develop infectious culture systems for all HCV subtypes, since patient isolates are not viable in culture. Further, even though HCV clones generated from consensus sequences of patient isolates could be infectious in vivo, such clones are rarely viable in cell culture, except for the JFH1 strain (genotype 2a), which can propagate in culture ,25. TakiHere, we aimed at filling this void by developing HCV genotype 4d infectious cell culture systems and using these new systems to test efficacy of DAAs and neutralization capacity of broadly reactive human monoclonal NAbs and NAbs in plasma from HCV genotype 4a and 4d infected patients. In addition, using the novel 4d infectious systems, we investigated the neutralization capacity of patient plasma taken during the infection and after successful DAA treatments in a patient with two successive HCV genotype 4d infections.The consensus ORF sequence of a HCV genotype 4d strain, named DH13, was obtained from a chronic hepatitis C patient plasma sample, taken before the patient underwent DAA treatment. This patient, co-infected with HIV, was recruited into The Danish Database for Hepatitis B and C (DANHEP). The whole HCV ORF was amplified by reverse-transcription PCR (RT-PCR) using the same primers that are used for amplification of genotype 4a ED43 . The PCRAmplification of the 5′UTR of genotype 4d DH13, obtained from patient plasma, was performed as described previously  with somTo determine the sequence of the variable region and poly (U/C) tract of genotype 4d DH13 3′UTR, extracted viral RNA from patient plasma was denatured at 70 °C for 5 min followed by cDNA synthesis at 50 °C for 1 h using Maxima H Minus Reverse Transcriptase  and a genotype 4a X-region specific reverse primer 4drX-9584-RT . PCR ampTo determine the sequence of X-region of genotype 4d DH13 3′UTR, the extracted viral RNA was tailed with homopolymers of ATP at 37 °C for 10 min using Yeast Poly(A) Polymerase , followed by synthesis of cDNA using Maxima H Minus Reverse Transcriptase , and primer TS-O-00178  47]. Br. Br47]. 2. To analyze viral viability of recombinants, in vitro RNA transcripts were transfected into Huh7.5 cells using lipofectamine 2000 , and the presence of HCV antigen positive cells were determined by immunostaining with anti-HCV NS5A antibody [10FFU/mL, as described previously [The human hepatoma cell line Huh7.5 was cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum  and 100 units/mL of penicillin-streptomycin . All cultures were maintained at 37 °C and 5% COantibody  and antiantibody . When vieviously . To deteeviously .50) values were calculated.All dose-response assays with NS3A/4A protease- and NS5A- inhibitors were carried out as described previously ,49. Brie3 Huh7.5 cells plated the day before in poly-D-lysine-coated 96-well plates. Following 4 h infection at 37 °C, the cells were washed and incubated in fresh medium for a total infection time of 48 h. Cells were then fixed and stained with anti-HCV NS5A antibody [Neutralization was carried out as described . Brieflyantibody . The datMale and female Alb-uPA mice were maintained in a CB-17/Icr-Prkdcscid/scid/Rj (SCID) background with ad libitum access to food  and water. The animals were housed by gender (except for breeders) in Innovive IVC caging containing wood chip bedding, shelters, nesting material, and biting sticks on a 12-h light dark cycle. All experimentation was conducted during light cycle adhering to 3R principles and with procedures consistent with affirmative response to the ARRIVE 10 questionnaire. Human-liver chimeric mice were generated as previously described ,52. BrieFor intrahepatic inoculation of animals with full-length HCV RNA transcripts, the plasmid containing the 4d DH13 full-length clone (pDH13) was prepared using EndoFree Plasmid Maxi Kit  and sequence confirmed. One mouse was inoculated under isoflurane anesthesia with 10 μg of PBS diluted in vitro RNA transcript of pDH13. Blood was sampled weekly by vein puncture under anesthesia according to guidelines for blood sampling, processed for serum, and stored at −80 °C until use.®Fast Virus 1-Step Master Mix , and a primers/probe set listed in Total RNA was extracted from mouse serum using TRIzol LS/chloroform extraction, purified with the RNA Clean & Concentrator kit  and eluted in 25 µL nuclease-free water. To quantify HCV RNA, a one-step real-time RT-qPCR reaction was performed using the LightCyler 480 , the TaqManThe sequences of HCV ORF and UTRs were determined as described in We initially determined the full-length ORF consensus sequence of HCV genotype 4d (strain name: DH13) from a hepatitis C patient co-infected with HIV  (Genbank accession number OP555741). The genome translates into a polyprotein of 3006 aa. Compared to 11 genotype 4d sequences listed in the Los Alamos database (retrieved on 31 October 2022), the DH13 4d ORF shows 92–97% and 94–97% identities at nt and aa levels, respectively . We alsoCompared with the 4a ED43 prototype sequence , the 4d 10 genome equivalents per ml (GE/mL) at week 1, 2, 3, and 4, respectively. NGS analysis of the ORF sequence of virus recovered from the animal at week 4 showed that no additional coding mutations emerged with a frequency ≥5% (data not shown). We also analyzed the 5′- and 3′-UTR sequences from recovered viruses and confirmed that the sequences were maintained. These results indicated that the genotype 4d full-length DH13 clone was fully viable in vivo.We decided to generate a genotype 4d full-length clone containing genotype 4a ED43 5′- and 3′-UTRs (Genbank accession number OP555742), since the ED43 UTRs have been shown to confer viability of genotype 4a both in vivo and in vitro ,56. ThisUsing Huh7.5 cells, we next tested whether pDH13 was viable in vitro. However, after transfection of Huh7.5 cells with RNA transcripts, we could not detect any HCV antigen-positive cells. In two independent transfections, the full-length 4d recombinant remained non-viable during 2 weeks of follow-up. Therefore, we attempted to identify adaptive mutations permitting culture of pDH13. We have reported that recombinants with genotype-specific C5A, JFH1-NS5B, and JFH1-UTRs could be adapted, and the adaptive mutations identified in C5A recombinant viruses could also be valuable for subsequent development of full-length recombinant virus ,32,33,3410 FFU/mL. NGS analysis of recovered virus revealed that 12 mutations emerged with frequency ≥20% in the viral population, while the 3 introduced mutations were maintained at ~100% , did not yield any HCV antigen-positive cells during 2 weeks of follow-up after transfection of Huh7.5 cells with in vitro RNA transcripts. Thus, mutations are required for viability of the JFH1-based 4d(C5A) recombinant. We therefore introduced mutations A1671S(NS4A) and T1785V(NS4B) into 4d(C5A) (designated 4d(C5A)-2m). These mutations enabled viability in a genotype 4a(C5A) ED43 recombinant . In addiat ~100% .10 FFU/mL at day 8 (10 FFU/mL in the 8th passage. For 4d(C5A)-14m, the recovered virus yielded peak titers of ~4.6 log10FFU/mL in the 2nd passage and did not acquire any mutations ≥ 20% while it maintained introduced mutations , since this mutation emerged as putative NS5A RAS during the treatment of genotype 4a ED43 virus with NS5A inhibitors , designaat day 8 A. We perat day 8 B. The viutations C.We next tested whether the 12 mutations in 4d(C5A)-13m, except for JFH1 NS5B mutation Q2851H, also could confer viability of the 4d full-length DH13 recombinant. However, after transfection of Huh7.5 cells with RNA transcripts from pDH13 recombinant with these 12 mutations, we did not observe any HCV antigen-positive cells after 2 weeks of follow-up.Since there are no genotype 4d infectious systems that have been developed so far, we employed the developed 4d(C5A) recombinant virus to investigate the efficacies of DAAs and NAbs.The recommended DAA regimens for treatment of HCV genotype 4 include NS3/4A protease inhibitors (PIs) grazoprevir and glecaprevir, NS5A inhibitors ledipasvir, elbasvir, velpatasvir and pibrentasvir, and NS5B inhibitor sofosbuvir ,58. Howe50 values obtained for this virus were like those reported previously [Here, the ED43cc 4a virus was used for comparison . For theeviously . In addi50 values for ED43cc obtained here are similar with those reported previously [50 values (ED43cc vs. 4d(C5A)-13m) (50 (4d(C5A)-13m vs. 4d(C5A)-14m)  emerged during the treatment of genotype 4a ED43 virus with NS5A inhibitors . The M31eviously . Interes5A)-13m) . Importa5A)-14m) . Neverth0 values .To our knowledge, no HCVpp or HCVcc systems have been developed for genotype 4d. Thus, our 4d(C5A) infectious system provides the first culture model to test the efficacy of NAbs against this genotype. Among human monoclonal NAbs that have been isolated, AR3A, AR4A, and AR5A target conserved epitopes among HCV genotypes ,62,63. A50 values -13m, indicating that genotype 4a ED43 was less NAb sensitive compared with genotype 4d -12m was less sensitive to NAbs compared with 4d(C5A)-13m virus with ~8-fold increase in EC0 values C. Intereotype 4d C. NevertWe next tested neutralization by hepatitis C patient plasma NAbs. We chose to test plasma samples from 3 genotype 4a infected patients, including strain AA , and 2 pFor comparison, we also tested this hepatitis C patient plasma panel against ED43 (Core-NS2). At the same dilutions 1:50, plasma pt2 (4d) and pt9 (4a) neutralized this virus by <75%, while other plasma samples could neutralize by ≥80% C. In the50 values for AA (4a) and pt6 (4d) plasma, respectively  and pt6 (4d). Expectedly, both plasma samples could neutralize the 4a and 4d viruses in a dose-dependent manner D,E. Simiectively D,E. The 0 values D,E.In conclusion, the selected monoclonal and patient plasma NAbs could neutralize the 4a and 4d viruses, but with different capacity, and an adaptive E2 substitution influenced virus neutralization.The genotype 4d clone was generated from a patient who was found to be infected with the strain named DH13 in 2013. The patient was treated with DAA grazoprevir/elbasvir in 2015 and had SVR. However, re-infection with another genotype 4d strain was detected in 2016 . The patThese plasma samples provide a valuable source for neutralization analysis of genotype 4d by antibodies that developed during the infection. Thus, we analyzed neutralization capacity against 4d(C5A)-12m virus since this virus does not harbor mutation I414T that could affect neutralization sensitivity of the virus. We also included genotype 4a ED43 Core-NS2 for comparison.50 values compared with 4a ED43 Core-NS2 virus , demonstrating that this consensus sequence is fully viable in a liver organ. However, this recombinant was not viable in cell culture (in vitro), as is the case for most other HCV strains (except for JFH1) . This isOur developed 4d Core-NS5A infectious system contains authentic NS3/NS4A protease and NS5A domain I, which is valuable for evaluating PIs and NS5A inhibitors. However, it does not contain authentic 4d NS5B, thus limiting its use for studying NS5B inhibitors, including sofosbuvir. Among the current PIs used in the clinic, grazoprevir has not been investigated in vitro for genotype 4d, while glecaprevir has been tested using a 4d replicon . In the For antibody neutralization, we found that there were differences in neutralization of genotypes 4a and 4d. For monoclonal NAbs AR3A and AR5A, genotype 4d is more sensitive than the genotype 4a virus. However, for patient plasma, the genotype 4d is overall less sensitive than genotype 4a. In both cases, the tested 4d(C5A)-12m and 4a Core-NS5A viruses did not harbor the E1 and E2 mutations at consensus level, which could alter neutralization sensitivity of the virus . TherefoAs mentioned, the envelope E1 and E2 mutations could influence neutralization sensitivity of the virus, as investigated previously . In the The 4d DH13 clone was generated from a chronic hepatitis C patient (sample obtained in 2013). However, following treatment induced SVR, this patient was re-infected with another 4d isolate. In our study, we observed clear differences in neutralization when comparing plasma taken in 2013 with plasma collected at later timepoints. The plasma obtained in 2013 had enhanced neutralization of 4a ED43 virus compared with 4d virus, while the neutralization difference was minor for plasma taken by the time of re-infection. It suggests that the heterogeneity of the two 4d isolates that infected the patient influences humoral immune response, resulting in production of different epitope-targeted antibodies, which is highly valuable information for vaccine design. In addition to neutralization differences, the antibody activity seems to be maintained for a long period of time, since we observed no major difference in neutralization for plasma taken more than 7 years apart. In future studies, it would be of interest to test whether the neutralization will be maintained, even though the patient was successfully treated with DAA . These dIn summary, we have developed an efficient genotype 4d Core-NS5A infectious culture system, providing a highly valuable and relevant model for testing of DAA efficacy and assessment of antibody neutralization. We showed that the recommended PIs and NS5A inhibitors for treatment of genotype 4 infection were efficient against both genotypes 4a and 4d without major differences in efficacy. Both genotypes were also effectively neutralized by monoclonal antibodies and plasma isolated from genotype 4a and 4d patients, though the sensitivity of the virus showed differences. Finally, the neutralization capacity was maintained during and after the infection was cleared by DAA treatment. These data would be useful for the use of DAA in the clinic, as well as for informing vaccine design, which is important for elimination of HCV as a public health threat.


Recently, the oncogenic role of lemur tyrosine kinase 3 (LMTK3) has been well established in different tumor types, highlighting it as a viable therapeutic target. In the present study, using in vitro and cell-based assays coupled with biophysical analyses, we identify a highly selective small molecule LMTK3 inhibitor, namely C36. Biochemical/biophysical and cellular studies revealed that C36 displays a high in vitro selectivity profile and provides notable therapeutic effect when tested in the National Cancer Institute (NCI)-60 cancer cell line panel. We also report the binding affinity between LMTK3 and C36 as demonstrated via microscale thermophoresis (MST). In addition, C36 exhibits a mixed-type inhibition against LMTK3, consistent with the inhibitor overlapping with both the adenosine 5′-triphosphate (ATP)- and substrate-binding sites. Treatment of different breast cancer cell lines with C36 led to decreased proliferation and increased apoptosis, further reinforcing the prospective value of LMTK3 inhibitors for cancer therapy. Protein kinases are a large family of enzymes responsible for catalyzing protein phosphorylation. They are involved in critical mechanisms regulating different cellular functions, including proliferation, cell cycle, apoptosis, motility, growth, and differentiation . The derLemur tyrosine kinase 3 (LMTK3) is a dual specificity serine/threonine kinase composed of a transmembrane helical segment, a kinase domain, and a C-terminal intrinsically disordered region . StudiesRecently, using robust in vitro and cell-based screening and selectivity assays combined with biophysical analyses, we identified and characterized a highly selective small-molecule adenosine 5′-triphosphate (ATP)-competitive LMTK3 inhibitor, namely C28, that acts by degrading LMTK3 via the ubiquitin-proteasome pathway . Mo. Mo32P γse assay E. More s50 by tracking the cellular viability of FDCP-1 parental and FDCP-1/BRC-LMTK3. As shown in Following this, we used the interleukin-3 (IL-3)-dependent murine bone marrow-derived cell line FDCP-1 and engineered an LMTK3-transformed clone (FDCP-1/BCR-LMTK3) that relies on the constitutive expression of catalytically active LMTK3 for its survival and proliferation, as described previously . Using tm (0.486 μΜ from 0.364 μM in the absence of C36) with a significantly lower Vmax (26.1 μmol/min from 59.0 μmol/min in the absence of C36). Next, we investigated the effect of increasing concentrations of ATP at a fixed substrate (HSP27) concentration of 0.6 μM. Similarly, the presence of C36 resulted in a significant increase of the apparent Km (0.048 μM from 0.023 μM in the absence of C36) accompanied by a substantial decrease in Vmax  spectroscopy. Both methods displayed a single transition in the thermal melting curves, while the thermal unfolding of LMTK3 was irreversible due to protein aggregation. Our results revealed a minimal influence of the thermal stability of LMTK3 in the presence of C36, with the thermal shift assay indicating a decrease in the TConsidering that LMTK3 in the absence of ATP and substrate is mainly in the inactive state, the results of the thermal shift assay and CD spectroscopy experiments suggest that C36 has a poor affinity for the inactive state of LMTK3 in solution. Taken together, these results indicate that C36 has no effect in the thermodynamic stability of inactive LMTK3, which contrasts with C28, which displayed a statistically significant stabilization of LMTK3 in the absence of ATP and/or substrate [314 [With the aim to present a putative model of LMTK3 with bound C36 that is in accordance with the above-mentioned results, we prepared a homology model of LMTK3 in the active state and carried out docking of C36. The inactive state of LMTK3 remains the only available X-ray structure where the ATP-binding site is occluded by the DYG-motif Tyr314 . Conside50 values ranging from 16.19 µM to 18.38 µM. Following this, we submitted C36 to the Developmental Therapeutics Program (DTP) of the National Cancer Institute (NCI) and screened it against a panel of 60 human cancer cell lines [We then investigated the potential use of C36 as an anticancer strategy by examining the viability of various BC cell lines in the presence of increasing concentrations of C36. As shown in ll lines . InteresFinally, we investigated the apoptotic properties of C36 in the aforementioned BC cell lines using annexin V and 7-AAD (7-amino-actinomycin D) staining. As shown in −6 cm/s), which could limit its absorption in in vivo studies, although its efflux ratio of 2.1 is not excessive . Our results show that C36 was metabolized, relatively quickly, with a half-life value of 22 min and a high intrinsic clearance (Clint) value of 132 µL/min/mg . This mixcessive .Despite the increasingly established role of LMTK3 in several cancer types and its central role in a number of well-described signaling pathways ,10,15,20Recently, we reported the first tool compound C28) against LMTK3 that displays anticancer activity in a variety of cancer cell lines and in vivo BC mouse models [8 against−6 cm/s), work is currently underway, focusing on the design of C36 analogues and testing their effects in xenograft models of BC. Additional testing in other types of non-cancerous cell lines will provide further validation regarding C36 selectivity against cancer cell lines.The analysis of C36 metabolic stability was performed by incubating this drug with mouse hepatic microsomes. The results indicate C36 was metabolized relatively quickly. This cell-based system is not the ideal indicator for mouse in vivo studies; however, it is a commonly used steppingstone that can correlate well with liver microsomal stability in human and in vivo activity in mice ,35,36. MFuture work will focus on studying the specific molecular interactions between C36 and the kinase domain of LMTK3 by performing co-crystallization experiments. Investigating which amino acid residues are involved will likely shed light onto the mechanism of action of C36, furthering our knowledge. From our preliminary data presented in this manuscript, C36 decreases the rate of HSP27 phosphorylation by LMTK3 kinase domain. Therefore, additional experiments might include co-crystallizing the entire complex (LMTK3-HSP27-C36) in order to understand, in more detail, the specific molecular interactions involved. Additional C36 analogues are also being synthesized to improve the binding affinity of the drug to LMTK3 and to increase its inhibitory properties against LMTK3 activity both in vitro  and in breast cancer cell lines.Since LMTK3 has been shown to have a fundamental role in breast cancer progression and since there are no current drugs available targeting this oncogenic kinase, LMTK3 inhibitors could represent a valid alternative treatment to breast cancer patients. More specifically, LMTK3 inhibitors could be combined alongside aromatase inhibitors (AIs) as an alternative to treatment with CDK4/6 inhibitors to improve patient outcome in estrogen receptor positive (ER+) BC ,38. LikeIn addition, since the mechanism of emergence of endocrine and chemotherapy resistance in BC remains largely unclear , there i®, Genentech, San Francisco, CA, USA)) and chemotherapeutic agents ) has already been applied for the treatment of TNBC [Finally, immunotherapy has become an established mainstay in cancer treatment and new drugs are being promptly developed for use in clinical settings . Monoclo of TNBC . Based o of TNBC  to improMCF7, T47D, and MDA-MB-231 cell lines were purchased from ATCC. MCF7 and MDA-MB-231 were maintained in low glucose DMEM  supplemented with 10% FBS  and 1% Penicillin/Streptomycin . T47D cell line was maintained in RPMI-1640 medium  supplemented with 10% FBS  and 1% L-glutamine/Penicillin/Streptomycin solution .p values < 0.05 were considered to be statistically significant.Cells were treated with increasing concentrations of C36 for 96 h. After collection, cells were stained with the Muse Annexin V Dead Cell Kit according to the manufacturer’s protocol . Cells were then analyzed using the Muse Cell Analyzer (Millipore). Statistical analysis was performed with GraphPad Prism 8.0.1 software. In particular, one-way ANOVA analysis of variance with Dunnet post-hoc test for multiple comparison was performed. Statistical significance refers to the sample compared to the control (DMSO). 50 values as previously described [Cell viability assay was performed as previously described . Mammaliescribed .32P γ-ATP in vitro kinase assays were performed in-house, as we have previously described [escribed . The int50 values, which usually depend on the ATP concentration. In particular, “hits” are detected by measuring the amount of kinase captured in test versus control samples by using qPCR, which is a method that detects the associated DNA label [The selectivity profiling of C36 kinase inhibitor at 5 µM was analyzed using DiscoverX KINOMEscan competition binding assay against a panel of 403 kinases . The KINNA label .d values for binding.Purified LMTK3 protein was labelled with an NHS-647 red dye , following the manufacturer’s protocol. Serial dilutions of C36 (200 µΜ–0.61 nM) and C28 (200 µΜ–3.05 nM) in MST buffer  were mixed with 50 nM NHS-647-labeled LMTK3 and loaded into standard glass capillaries . The final DMSO concentration was kept below 5%, as indicated by Ref. . Thermopv/v) DMSO, 4.3 µΜ LMTK3, and 10× SYPRO orange). The temperature range spanned from 25 °C to 80 °C at a scan rate of 1 °C/min. Data analysis was performed in LightCycler 96  software using the melting curve analysis, and Tm values were determined as the first negative derivative of the fluorescence with respect to the temperature.A thermal shift assay was performed using Roche LightCycler 96 real-time polymerase chain reaction (RT-PCR) instrument, with excitation and emission wavelengths set to 533 and 572 nm, respectively. Solutions comprising 16 µL of 5.4 µM LMTK3 in 200 mM tris buffer (pH 8.0), 200 mM NaCl, and 4 µL of 50× SYPRO orange  and 0.2 µL of either dimethyl sulfoxide (DMSO) or C36 in DMSO  or 8.3 µΜ C36 in DMSO (0.4%) to a total volume of 120 µL in 0.1 cm cuvettes. Data analysis was performed in GraphPad Prism 8.01 software by fitting data in the transition region to a Boltzmann sigmoidal. Apparent Tm values were determined as the point at which the transition was 50% complete.CD spectroscopy was performed using a Jasco J-715 instrument  equipped with a PTC-348 temperature control unit. Temperature increased from 20 °C to 90 °C at an increment of 1 °C/min, and data points were acquired every 0.2 °C by monitoring a wavelength of 230 nm. For thermal stability experiments, LMTK3 samples of 5.4 µΜ in 200 mM tris buffer (pH 8.0) and 200 mM NaCl were treated with either DMSO 0.4% (rmsd tolerance of 2.0 Å (−1 × Å−2) using AMBER v16  [The homology model of LMTK3 in the active state and the X-ray structure of the kinase domain of human insulin receptor (IRK), in complex with ATP and a peptidic-substrate (PDB ID: 3bu5) , were prof 2.0 Å , and theof 2.0 Å F. The moof 2.0 Å E was genCA, USA) .5 cells per well and used in confluent monolayers after a 21-day culture at 37 °C under 5% CO2. Test and control compounds , prepared in DMSO, were added  to donor compartments of the Transwell plate assembly in assay buffer  for both apical to basolateral (A > B) and basolateral to apical (B > A) measurements. Incubations were performed at 37 °C, with samples removed from both donor and acceptor chambers at T = 0 and 1 h and compound analyzed by mass spectrometry (LC-MS/MS) including an analytical internal standard. Apparent permeability (Papp) values were determined from the relationship:The bi-directional Caco-2 cell permeability assay was performed as described in the BioFocus DPI Ltd. Standard Operating Procedure, ADME-SOP-49. Caco-2 cells (ECACC) were seeded onto 24-well Transwell plates at 2 × 10V is the volume of each Transwell compartment , and concentrations are the relative MS responses for compound  in the donor chamber before incubation and acceptor chamber at the end of the incubation.2).Area = area of cells exposed for drug transfer (0.33 cmapp B > A/Papp A > B) were calculated for each compound from the mean Papp values in each direction. A finding of good permeability B > A, but poor permeability A > B, suggests that a compound is a substrate for an efflux transporter, such as P-glycoprotein.Efflux ratios  was added to the apical buffer in all wells to assess viability of the cell layer. As LY cannot freely permeate lipophilic barriers, a high degree of LY transport indicates poor integrity of the cell layer and wells with a LY PCompound recovery from the wells was determined from MS responses  in donor and acceptor chambers at the end of incubation compared to response in the donor chamber pre-incubation. Recoveries < 50% suggest compound solubility, stability, or binding issues in the assay, which may reduce the reliability of a result.Microsomal stability assays were performed as described in the BioFocus DPI Ltd. Standard Operating Procedure, ADME-SOP-84, using pooled hepatic microsomes from mouse . Test and control compounds (dextromethorphan and midazolam), prepared in DMSO, were incubated at an initial concentration of 1 µM  with microsomes (0.25 mg protein/ml) at 37 °C in the presence and absence of the cofactor, NADPH (1 mM). Aliquots were removed at 0, 5, 10, 20, and 40 min for termination of reactions and compound extraction with acetonitrile containing an analytical internal standard. Samples were centrifuged and the supernatant fractions were analyzed for parent compound by mass spectrometry (LC-MS/MS).The amount of compound remaining (expressed as %) was determined from the MS response in each sample relative to that in the T = 0 samples .1/2 (min) = −0.693/λ, where λ is the slope of the Ln % remaining vs. time curve.Ln plots of the % remaining were used to determine the half-life for compound disappearance using the relationship: tThe in vitro intrinsic clearance (CLint)  was calculated using the formula: CLint = 0.693 × 1/t½ (min) ×  × 1000.The NCI-60 panel of tumor cell lines utilizes a variety of different cancerous cell lines to identify and characterize novel compounds that inhibit the growth or exert a lethal effect on these tumor cells. This screen encompasses 60 cell lines from leukemia, melanoma, and cancers of the colon, brain, ovary, lung, prostate, breast, and kidney. In our case, 60 different cell lines were treated with 10 µM C36 for 24 h. Following this, growth inhibition and lethality were measured .


Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines’ sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic. As inteThe contribution of multi-omics to support early phases of drug discovery is growing exponentially in the era of precision medicine . CombineM) and bi-functional (B) 3-CePs bearing a single or double alkylating units  (The selected  B3-CePs  were sho B3-CePs . From th0 for B)  to illus50s in BxPC-3 and at a 10-times higher concentration   . Surprisred to B . These rM induced a persisting block in G1 throughout the observation time in BxPC-3 cells, this block was absent in HCT-15 cells. In contrast, B induced an early G2/S block in HCT-15 cells (6 hr), which was not observed at later time points, while such a block was most obvious at 12 hr for BxPC-3 cells. Despite similar DNA damage accumulation, these findings clearly indicated a different behavior for the two cancer cell lines in terms of cell cycle progression after treatment with the two 3-CePs.Since alkylating agents are known to alter the progression of the cell cycle , we nextTo determine the additional mechanisms explaining differential sensitivity to 3-CePs, we measured the activation of the DNA repair machinery as another key aspect in the cellular response to genotoxicants . To veriTaken together, these results indicated that cell-specific mechanisms after the first event of DNA damage are responsible for the different sensitivity to 3-CePs.M (10 nM) or B (200 nM) for 6 hr and 12 hr  of all transcripts separated samples within each cell line according to treatment and time point , suggestGene ontology (GO) enrichment was performed on the DE genes to determine signaling pathways and transcriptional programs explaining the observed differences. In a first explorative approach, we generated the union of DE genes per cell line irrespective of compound and time point, which allowed us also to distinguish between cell type-specific and shared DE genes . The mosUnexpectedly, we identified a strong translational response in BxPC-3 cells after treatment, a process which is typically attenuated in stress conditions, as was the exposure to our DNA damaging agents, to allow proper recovery of the protein quality control machinery . In contTo further characterize these transcriptional changes over time in a cell type-specific context, we grouped the DE genes at 6 hr and 12 hr in modules according to the similarity in their expression profiles and performed a functional enrichment on genes with similar expression patterns . Genes iAlso, HCT-15 cells upregulated clusters of genes mediating DNA repair, protein stability, and mitochondrial activity as early as 6 hr of treatment, suggesting this time point as the most informative to describe the response to 3-CePs  and other recombinational pathways such as homologous repair (HR) (e.g. MMS22L and BARD1) and fanconi anemia (FA) (e.g. BRIP1 and FANCM), all better suited for the efficient repair of bulky lesions and highly toxic DSBs and crosslinks (APEX1 and UNG) and MMR (mismatch repair) (e.g. MSH6 and EXO1), which contribute to the repair of smaller lesions and mismatches , other genes mediating protein catabolism (e.g. LAMP2 and CUL3), and ER morphogenesis (e.g. RTN4) , and PPP1R15A (GADD34) were significantly upregulated after 6 hr of exposure only in this cell line  . Interesg. RTN4) . Even moell line . These gell line . This meell line .SCD) enzyme, for which we detected a significant upregulation of the respective transcript in HCT-15 , B 6 hr), and As a first level of integration, we identified genes with concordant regulation in RNA- and ATAC-seq upon treatment. In this pairwise integration, we compared the direction of transcriptional regulation of genes to the accessibility of their promoters, as specified in the Methods section and shown in M, and similarly for B (RNPS1), apoptosis (e.g. AEN), BER (e.g. APEX1), and cell cycle regulation (e.g. CDC45) at 6 hr of treatment in BxPC-3 cells. Meanwhile, at the same conditions, genes regulating the DDR (e.g. MDM2), proteostasis, and redox balance (e.g. GPX1) were found to be downregulated consistently in both omic layers.Overall, the integration efficiently identified key regulators of previously described processes, once more highlighting the value of applying multi-omics to gain a higher resolution and robustness in the analysis of drug responses. As described in ly for B , we obsePSMA3), DNA repair (e.g. DTL), and lipid metabolism, such as the previously described SCD. Interestingly, these cells also turned out to activate TGF β signaling (e.g. BMPR1A), an important player in cancer drug resistance, in response to B (CARMIL1) (CALCOCO2) as a putative pathway accounting for the enhanced catabolism in HCT-15 cells  . AdditioCARMIL1) , suggest15 cells .Collectively, pairwise integration of RNA- and ATAC-seq shed light on genes with robust regulation at the transcriptional and chromatin level, adding further details to the previously identified response pathways.Through the pairwise approach, we identified genes with both transcriptional and chromatin regulation which significantly contributed to the observed cellular response. We further evaluated the crosstalk between RNA- and ATAC-seq at a different level by focusing on groups of genes co-regulated in the two omic layers. The identification of genes sharing similar regulation across conditions either at the transcriptional or chromatin level would maximize the detection of interacting pathways and regulatory processes, e.g., as a result of chromatin changes in promoters tuning the transcription of a certain gene set. This approach, which we termed crosswise integration, was achieved by a combination of horizontal and vertical construction of co-expression network analysis (hCoCena and vCoCena).Both algorithms are based on CoCena, which inspects the pattern of gene regulation across conditions in a single transcriptome dataset. Its evolution hCoCena can inspect gene regulatory patterns across omic datasets of the same type and integrate them in a single network . InsteadAs a first step, we created separate co-expression networks for the RNA- and ATAC-seq layers . For eacB in HCT-15, while the latter ATAC-seq-based module included peaks condensing after 6 hr only in BxPC-3, confirming the latter cell line as refractory to a rapid engagement of its protein catabolism apparatus. Another mostly RNA-seq-based module validating our previous approach was the maroon module, upregulated after 6 hr in BxPC-3, containing genes involved in ribosome biogenesis, BER, and apoptosis. Furthermore, the dark green and dark gray ATAC modules, downregulated in BxPC-3 cells, include genes modulating redox metabolism and ER stress.The approach combined genes sharing similar regulation in the respective omic dataset, as approximated by the group fold-change (GFC) pattern, with the postulate that genes grouped together cooperate in specific cellular processes. To define the underlying mechanisms, GO enrichment was performed on genes included in each of the modules, and representative biological terms for the most relevant clusters were reported in M. Interestingly, these hits belong to the Wnt signaling pathway, well-known for regulating ribosome biogenesis and cell growth, adding further information on our initial findings. In addition, the indian red module, upregulated in HCT-15 cells, points toward the tumorigenic JAK-STAT as an additional pathway modulating drug resistance . According to our hypothesis, genes that belong to these modules, coming both from RNA- and ATAC-seq analyses, are expected to be the major determinants of the differential susceptibility in the two cell lines.First, we selected vCoCena clusters based on the highest difference in regulation between the two cell lines after treatment with 2FC]>1), and sorted out only those belonging to previously selected modules. This approach resulted in a subgroup of genes with different basal expression in BxPC-3 cells as well as a sufficiently compound- and cell line-specific regulation upon perturbation. This perturbation-informed signature was composed of 307 genes upregulated (signature up) and 123 genes downregulated (signature down) in the high-sensitive BxPC-3 cells  on publicly available transcriptomes of common cell lines, or less sensitive  to compound .Interestingly, the predicted tumor type with the highest sensitivity turned out to be KIRC, demonstrating that the designed signature was not driven by the original cell type of the cell lines used for its extrapolation and could go beyond the original cancer type. At the same time, PAAD and COAD (as BxPC-3 and HCT-15 cells) were still among the most and least sensitive, confirming that cell type intrinsic determinants of susceptibility exist and are represented in our signature. Interestingly, intra-tumor variability resulted in a continuous distribution of samples scores within each cancer group, confirming the importance of clinically translating such predictions beyond the tumor type to better address patient-specific therapeutic needs.To enlarge the accessibility and clinical translatability of our framework, we finally introduced a LASSO regression model to predict the sensitivity of tumor samples in the external reference dataset . We traiIn detail, TCGA samples were labeled according to the calculated continuous sensitivity scores. Next, the model was trained on 80% of the data and tested on the remaining 20%, which efficiently predicted drug sensitivity within the test samples  . Notably50s reported in the Genomics of Drug Sensitivity in Cancer database   . Then, w: 55 µM) , C, D, u: 55 µM) . At thisdatabase . ResultsOverall, we demonstrated how to further employ the integrated RNA- and ATAC-seq information to assemble an accurate and clinically-accessible predictive strategy capable of orienting drug development and supporting medical practice in the context of precision oncology.Despite the advances of the last decades, efforts are required to expedite routine use of omic-scale approaches in preclinical and clinical settings. Recent work illustrated the potential for omics technologies to accelerate the process of drug discovery from the initial identification of candidate lead compounds up to their preclinical and clinical development . FurtherHere, we combined transcriptome and chromatin accessibility analyses within perturbation experiments to investigate the specific activity profile of 3-CePs, a new class of potential anticancer agents acting as DNA alkylators . Our anaBeyond validating the described results, the analysis of chromatin accessibility was first employed to identify genes with concordant transcriptional and epigenetic regulation, a step we called pairwise integration. The approach revealed both epigenetic and transcriptional regulation of key response pathways, identifying apoptotic and ribosome biogenesis mediators upregulated in BxPC-3 and downregulated in HCT-15, as well as redox balance and proteostasis hits upregulated in HCT-15 and downregulated in BxPC-3. In addition, actin dynamics, shown to assist DSBs repair , were idTo further evaluate the interaction between transcriptional and chromatin accessibility responses, we proposed here a new versatile approach for the crosswise integration of RNA- and ATAC-seq, based on vCoCena. This approach identified modules of genes co-regulated in the two omic layers across the analyzed experimental conditions. Combined with the pairwise integration, this standalone method not only recapitulated the result of the independent transcriptomic and epigenomic analysis, but also highlighted additional pathways, e.g., EMT and TGF β signaling, which modulate the response to the compounds. Indeed, a protumorigenic role was established for TGF β in mediating carcinogenicity linked to epithelial-mesenchymal transition, both processes that could additionally explain the more efficient response of HCT-15 cells to 3-CePs . EfficieTo further assist the development of 3-CePs, we set up a pilot sensitivity prediction framework readily transferable from the bench to the clinics. We designed a perturbation-informed signature derived from the integrated omic layers filtering the differentially expressed genes between the two cell lines at a steady state for those specifically involved in the cellular response to the treatment. Though based on a limited number of perturbed profiles, this gene signature predicted with high precision the sensitivity to 3-CePs only relying on the untreated transcriptome of test cell lines. Not only did this approach work when applied to our candidate drugs, but it also efficiently predicted sensitivity to the clinically established antineoplastic agent cisplatin. The possibility to improve predictions from basal transcriptomes sounds attractive from a clinical perspective since it overcomes the need to screen for thousands of drugs and collect the same amount of profiles from limitedly-available patient samples, such as biopsies . AppliedIn this study, we combined transcriptomic and epigenetic data to guide our exemplary analysis. Nevertheless, the modularity of our framework allows, with only minimal adjustment, its application to other omic technologies or experimental designs. The pipeline showed to be versatile, being applicable to other drugs and, as a consequence, generalizable to other diseases and target cell populations. Indeed, the vCoCena integration, which is instrumental for both the biological interpretation of the data and the definition of the perturbation-informed signature, is agnostic of the type of data used as soon as this is reduced to a network of co-regulation.In conclusion, we present a complete end-to-end workflow to implement the use of multi-omics in drug development, providing a human-readable toolbox to interrogate pharmacological questions in both preclinical and clinical settings. We applied this framework to understand the MoA of 3-CePs revealing the cellular determinants of sensitivity to this novel class of drugs and providing additional information for their clinical development as anticancer candidates. Given its versatility, we envision our workflow to be a broadly applicable resource to assist researchers in different steps of the drug discovery and development process.Colon , pancreatic , lung  carcinoma cell lines, human embryonic kidney  and acute T cell leukemia cells  were purchased from ATCC (American Type Culture Collection). Prostate (PC-3) and testis (NTERA-2) carcinoma cell lines were kindly provided by Prof. W. Kolanus , neuroblastoma (SH-SY5Y) by Prof. D. Schmucker , and epidermoid (A-431) carcinoma by Prof. G. Zunino . Cell lines were maintained in logarithmic phase at 37°C in a 5% carbon dioxide atmosphere using RPMI-1640 , DMEM , or Ham’s F-12K (for A549) media (by Gibco or Euroclone) containing 10% fetal calf serum, antibiotics (50 units/mL penicillin and 50 μg/mL streptomycin) and 2 mM L-glutamine (Euroclone).M (10 nM and 100 nM), B (200 nM and 2 µM), or DMSO 0.5%. After treatment, we measured the effect of double and single-strand breaks on the rate of unwinding of cellular DNA in denaturing alkaline conditions by monitoring the fluorescence of a dye that preferentially binds to dsDNA up to 20 min . The assay was performed following the protocol of The extent of early DNA damage induced by 3-CePs in treated cells was assessed by the Fast Micromethod single-strand-break assay. This approach can detect both single and DSBs, as well as alkali-labile adduct sites in the DNA of treated cells. 5000 cells/well were seeded in 96-well microplates and treated next day for 6 hr with M (10 nM), B (200 nM), or DMSO 0.5%. Cells were harvested, washed with PBS, fixed, and permeabilized with the Foxp3 transcription factor staining buffer set . In detail, cell suspensions were fixed for 1 hr at room temperature with FixBuffer, washed twice with PermBuffer, and stained with anti-human γH2AX AlexaFluor 488  for 1 hr at 4°C. After the first staining, cells were washed first with PermBuffer, then with PBS and stained secondly with PI . Samples were acquired on a BD Symphony instrument equipped with five lasers , the spectral overlap between the channels were determined with single stained samples using FACSDiva (v 9.1.2). Samples were analyzed in FlowJo . Events were gated first according to FSC-A and SSC-A and cleaned from cell doublets with three consecutive gates . The frequency of cells within each phase of the cell cycle was calculated using the PI-A signal with the FlowJo built-in algorithm (Watson model with constrained G2 peak). Three biological replicates were obtained per condition and unpaired two-tailed Student’s t-test was performed to assess statistical significance (p<0.05).Possible effects of 3-CePs treatments on the cell cycle distribution of both cell lines were analyzed by FACS, staining cellular DNA with the PI  dye. In addition, we monitored by antibody staining the phosphorylation of histone H2AX, upstream event of the DDR cascade, after 6 hr and 12 hr of treatment in order to investigate the ability of BxPC-3 and HCT-15 cells to detect DSBs. 200,000 cells/well were seeded in 12-well plates and treated next day for 6 hr, 12 hr, or 72 hr with M (10 nM), B (200 nM), cisplatin , or DMSO 0.5%. Both for RNA- and ATAC-seq samples, three experimental replicates were obtained for each condition in case of 3-CePs datasets, four replicates were instead produced for the cisplatin datasets.For both RNA- and ATAC-seq analyses, 300,000 cells/well were seeded in 6-well plates and treated next day for 6 hr, 12 hr, or 72 hr with At the end of the treatment, cells were washed, resuspended in 1 mL QIAzol reagent (Qiagen), and stored at −80°C. We extracted the RNA using the miRNeasy mini kit (Qiagen) and checked the RNA integrity and quantity using the tapestation RNA assay on a tapestation 4200 instrument (both from Agilent). We used 750 ng total RNA to generate NGS libraries using the TruSeq stranded total RNA kit (Illumina) following manufacturer’s instructions. We checked library size distribution via tapestation using D1000 on a tapestation 4200 instrument (Agilent) and quantified the libraries via Qubit HS dsDNA assay (Invitrogen). We clustered the libraries at 250pM final clustering concentration on a NovaSeq6000 instrument using SP and S2 v1 chemistry (Illumina) and sequenced paired-end 2×50 cycles before demultiplexing using bcl2fastq2 v2.20.At the end of the treatment, cells were washed, harvested, resuspended in PBS with 1 mM EDTA, stained with the LIVE/DEAD Near-IR fixable dye  for 10 min at 4°C, centrifuged and suspended in PBS with 1 mM EDTA. 20,000 living cells/sample were sorted by FACS and further processed for nuclei isolation and transposition reaction following the protocol of We generated ATAC-libraries from tagmented cells following the protocol of Buenrostro et al. We checked library size distribution via tapestation using D5000 assay (ATAC) on a Tapestation 4200 instrument (both from Agilent) and quantified the libraries via Qubit HS dsDNA assay (Invitrogen). We clustered the libraries at 250pM final clustering concentration on a NovaSeq6000 instrument using SP and S2 v1 chemistry (Illumina) and sequenced paired-end 2×50 cycles before demultiplexing using bcl2fastq2 v2.20.2FC-based ranks. All enrichment dotplots report the count and p-value associated with each term, when p<0.05. Representative enrichment terms in 2FC-based GSEA. Hierarchical clustering was applied to identify blocks of DE genes with similar regulations across conditions as reported in the presented heatmaps   using stheatmaps . In the dedup function, and the transposase-induced offset was corrected using the deeptools v 3.1.3 alignmentSieve function . All present peaks were used as input for PCA. The call for DARs was performed for all treated vs control comparisons (separate cell lines) considering a p<0.05 threshold. Only peaks mapping in promoters of protein-coding regions (according to ChIPseeker annotation) were considered for further functional analyses. GSEA based on the GO biological process database was employed for functional enrichments based on DAR-associated genes  tool which inspects the pattern of regulation of genes across conditions in a single transcriptome dataset. The core principles driving both network construction and gene modules detection by CoCena have been described previously . In thisSubsequently, inter-omic integration by vCoCena was applied to construct the final network. In this case, the correlation between the mean GFC pattern of modules belonging to the two layers was calculated to identify clusters of genes with similar regulation, suitable for crosswise integration. The GFCs are obtained by calculating the mean expression of a gene over all conditions and then computing the fold change of the mean gene expression within each condition from the overall mean . Edges fM and cisplatin, relevant modules from the crosswise vCoCena integration were selected as follows (M (or cisplatin) 6 hr treated groups (∆GFC [cell line]=GFC(M6h) − GFC [ctrl]). The early time point was selected to guide the signature construction since from upstream analyses it turned out to be the most informative of cell responses to 3-CePs. The threshold score was then calculated as the difference between the previously obtained ∆GFCs for the two cell lines (thrscore = ∆GFC [BxPC-3] − ∆GFC [HCT-15]). Modules with thrscore above q50, thus modules where the regulation was sufficiently different in the two cell lines after treatment with M, were selected . Genes from the identified modules were grouped together and further considered to drive the definition of our signature of interest.For the signature of sensitivity to  follows : for eac2FC]>1 [M] or >2.5 [cisplatin], padj <0.01). In fact, given the much higher availability and clinical spendability of RNA-seq compared to ATAC-seq profiles, the signature was finally constructed only from basal transcriptomes. In particular, we further selected among the identified module genes only those that were also DE between the two untreated controls, ending up with a restricted group of genes showing compound- and cell line-specific regulation upon perturbation but, meanwhile, a significantly different basal expression in BxPC-3 cells. This perturbation-informed signature for M was composed of 307 genes upregulated (signature up) and 123 genes downregulated (signature down) in the high-sensitive BxPC-3 cells  a random genes signature (composed by random genes among those annotated in the RNA-seq profile of HPA cell lines) and (2) a control signature composed by the top up- and down- log2FC DE genes between the two cell lines (listed in 50 was available in the Genomics of Drug Sensitivity in Cancer database. Only cell lines with IC50 no higher than the one of low-sensitive BxPC-3 cells (55 µM) were included in the correlation plot.To validate the predictive performance of the obtained signature, GSVA was performed both with up and down signatures on the basal RNA-seq profiles of cancer cell lines included in the HPA (human protein atlas) . A sensiGSVA (v 1.38.2) was applied also on basal transcriptomes of samples from the Cancer Genome Atlas TCGA database, and their sensitivity score was calculated as previously indicated. The relative distribution of samples from different tumor types in terms of calculated sensitivity score was plotted together with the indicated median value for each group, to identify possibly more susceptible tumor types.cv.glmnet function in glmnet package v 4.1 to assess lambda penalty, predict function in stats package v 4.0.3 for actual prediction)  . BrieflyM obtained from the newly constructed signature was validated on a subset of available cell lines included in the Human Cell Atlas. The selected cell lines spanned quite well between the max and min detected susceptibility scores. Here are the screened cell lines from the one predicted as most sensitive: PC-3, A549, A-431, SH-SY5Y, NTERA-2, and HEK-293. 5000 cells/well were seeded in 96-well microplates and after 24 hr treated with M 10 nM for 72 hr. Cell viability was assessed at the end of the treatment by MTT, following previously adopted protocols  in cell cycle and H2AX phosphorylation analyses between treated and control conditions (n=3). All correlation coefficients were calculated with a Pearson’s test. The adopted statistical tests, the considered significance levels, and the number of biological replicates are also reported in figure legends. Boxplots are in the style of Tukey, where the center of the box represents the median of values, hinges represent the 25th and 75th percentile, and the whiskers are extended not further than the 1.5×IQR (inter quartile range). The analysis was performed on R (v. 3.6.2 or 4.0.3): the specific packages used for the analysis, their version and relevant parameters used are explained in the Methods sections. All plots were generated with ggplot (v. 3.3.2) except for the heatmaps which were generated with the R package complexheatmap (v. 2.2.0). To ensure the reproducibility of the manuscript results, all the analyses were conducted within a containerized environment (Docker). RNA- and ATAC-seq analyses were performed with the docker image jsschrepping/r_docker:jss_R362 (https://hub.docker.com/r/jsschrepping/r_docker). The rest of the analysis was conducted with the image lorenzobonaguro/cocena:v3 (https://hub.docker.com/r/lorenzobonaguro/cocena) for compatibility with the CoCena pipeline.Unpaired two-tailed Student’s https://www.proteinatlas.org/about/download. The file RNA HPA cell line gene data contains transcript expression levels summarized per gene in 69 cell lines and is based on the Human Protein Atlas version 20.0 and Ensembl version 92.38.Similarly, the TCGA expression data from cancer cell samples (the Cancer Genome Atlas) were downloaded from the same web page of the Human Cell Atlas (Transcript expression levels summarized per gene in 7932 samples from 17 different cancer types). Data are based on The Human Protein Atlas version 20.0 and Ensembl version 92.38. Cisplatin IC50s in different cell lines can be accessed through the Genomics of Drug Sensitivity in Cancer database (https://www.cancerrxgene.org/compound/Cisplatin/1005/overview/ic50) . The hCoCena script is accessible at https://github.com/MarieOestreich/hCoCena (swh:1:rev:0ff2b77fa7371ce88801b5798dd3c87e0b03d2b1) . Supplementary Data are available as .xlsx or .pdf files.All raw data included in this study are available at gene expression omnibus (GEO). For 3-CePs, raw RNA-seq data and count matrix are under the GEO accession number GSE179057. Raw ATAC-seq data and peak matrix are available under the accession number GSE179059. Both datasets are collected in a GEO SuperSeries (GSE179064). Cisplatin RNA-seq data and count matrix are available at the accession number GSE207611, the raw ATAC-seq data and peak matrix are available under the number GSE207607. Both are collected in a GEO SuperSeries (GSE207612).The cell line expression data employed in the prediction pipeline were downloaded from ew/ic50) . Codes t To test differential anticancer drug effects on different tissue types, and to understand drug response mechanism, the authors set up a series of RNA-seq and ATAC-seq experiments on drug responsive and non-responsive cell lines. Then they conducted bioinformatic analyses to pinpoint networks that are altered in responsive vs non-responsive cell lines. Remarkably, they used their analytic results to calculate tumor- and sample-specific response to the drug. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Murim Choi as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Mone Zaidi as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Zilu Zhou (Reviewer #2); Jocelynn Pearl (Reviewer #3).Thank you for submitting your article "Decoding mechanism of action and susceptibility to drug candidates from integrated transcriptome and chromatin state" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:eLife. To briefly summarize their concerns:The reviewers thought that the quality of the manuscript is high, but also thought that it contains several aspects of potential improvements to assure publication in 1) Design aspect: biological and clinical performance of 3-CePs is not clearly described. Is there any data that illustrates that HCT-15 is low-sensitivity vs BxPC-3 is high-sensitivity?2) Analytic aspect: is this approach capable of finding signals that are already constitutively expressed in different cell lines? Need more explanation for vCocena and hCoCena, and what are their differences with CoCena2. Were the tests corrected for multiple testing?3) Application aspect: What about its prediction accuracy within an individual cancer subtype? Will it achieve high prediction accuracy on intra-tumor type heterogeneity? Can the algorithm be applied to other drugs in this same class?Reviewer #1 (Recommendations for the authors):The crosswise approach is not easily comprehendible. It appears to be a network analysis on the union of genes from RNA-seq and ATAC-seq. I wonder if the introduction can be more clear and state uniqueness compared to existing approaches.Overall, figure quality is high. However, color coding in figures  is unique but not intuitive. Therefore, I had to refer to legends all the way to the last figure. I would suggest changing the color scheme to more intuitively or just use numbers/texts.Reviewer #2 (Recommendations for the authors):Overall I found this manuscript very interesting to read and significant to the field. However, please consider the following questions/comments:1. Authors mentioned that HCT-15 is low-sensitivity vs BxPC-3 is high-sensitivity. Is there any data that illustrates this point? Or any description of the phenotype after treatment?2. Can the authors describe more about vCocena and hCoCena in Method/Supplement, and what are their differences with CoCena2? I wasn't able to find the answer from the provided citation.3. I am also confused about Figure 5B panel 1 on GFC. I thought GFC is a relative fold change compared to treatment to control. What is the control group in this case to calculate GFC?4. The pink module is clearly of interest as it showcases the necessity of this multi-omic approach. More generally, can we label all the modules that are only identifiable cross-wise but not by single omic? Can we show a few more detailed examples here? Why are they identified now but not by the single omics approach?5. The prediction algorithm at the end shows good prediction power across cancer types. What about its prediction accuracy within an individual cancer subtype? Will it achieve high prediction accuracy on intra-tumor type heterogeneity?Reviewer #3 (Recommendations for the authors):I enjoyed reading your work and the approach you took to integrating multi-omics data to better understand the mechanism of action and epigenetic signatures of 3-CePs in cancer cell lines. The manuscript was clearly written, and I appreciated the thorough results and methods sections which allowed me to clearly follow the majority of the steps taken.As for major revisions, I believe it would be valuable if the authors would test their sensitivity model for compound B in addition to compound M in cell lines described on page 12 and in Figure 6D.It is great to see your work in this space and I am hopeful that it further strengthens the use of these data types and analysis methods for improving the drug development process. I will include my general thoughts and suggestions below.General ThoughtsThe paper is long – especially the Results section. But I think for this style of work it is better to have a longer, more thoroughly described results and methods section than to leave out key details.I would prefer if the paper included p-values for key findings in the text of the article instead of having to locate them in the supplement or figures. I did not see FDRs or q values mentioned throughout the paper/findings – were these calculated? Was there a reason the authors chose to report p-value<0.05 findings as opposed to an FDR/q-value threshold? I find that with gene set enrichment analyses, it can be important to use FDR.With regards to the crosswise integration method performed, the authors describe a threshold minimum of 15 nodes per cluster on page 23, line 757 of the manuscript. How was this threshold selected? It would be helpful to mention the total sample size input for constructing the network in these sections.For the sensitivity signature, the authors selected LASSO regression – did the authors consider linear regression? How was LASSO chosen?For the genes included in the perturbation-informed signature , what was the p-value and fold change thresholds used?For the sensitivity score (described page 12) – do the authors think that this is the ideal model for calculating a sensitivity score? Did they play around with changing the input for the sensitivity model or the model itself? It would be helpful to understand in greater detail why the authors felt that this was the best input for the model.The validation of the sensitivity prediction on cancer cell lines is given a short, one-sentence description in the results. This could be better explained.In the discussion and in other locations in the text, the authors describe their approach as 'efficient and versatile' which I think generally ignores the fact that this required next-generation sequencing, two assay types  and in-depth systems biology approaches. Is the argument that the network model is now versatile in that it can be applied to other drugs in this same class? Or that the approach can be borrowed by other groups for other classes of drugs and sample types?In the methods section, there were a few details I would like to see added. Product numbers including the ATCC product #s, propidium iodide product #, RNA tape station assay. EDTA concentration in ATAC-seq. For clarity, I recommend breaking the RNAseq and ATACseq methods sections apart. Please also include the sample numbers per group that were sequenced. This was hard to find. Essential revisions:The reviewers thought that the quality of the manuscript is high, but also thought that it contains several aspects of potential improvements to assure publication in eLife. To briefly summarize their concerns:1) Design aspect: biological and clinical performance of 3-CePs is not clearly described. Is there any data that illustrates that HCT-15 is low-sensitivity vs BxPC-3 is high-sensitivity?We apologize for not being clear enough when introducing previous findings on the differential sensitivity of HCT-15 and BxPC-3 cancer cell lines to 3-CePs. We now include an introduction to the previous work done by our lab in the revised manuscript  showing preferential activity of these agents against the pancreatic cancer cell line in both 2D and 3D cancer models .2) Analytic aspect: is this approach capable of finding signals that are already constitutively expressed in different cell lines?intrinsic characteristics of the cell line. For this reason, we built the sensitivity signature on basal transcriptome profiles, where we prioritized a subset of genes on perturbational evidence .We thank the reviewers for pointing out such an interesting concept. The premise for the developed approach is that factors determining the overall cellular sensitivity to a treatment must be determined by basal Beyond signature genes, we show in Need more explanation for vCocena and hCoCena, and what are their differences with CoCena2.We apologize to the reviewers for not having properly described the difference between CoCena and the other two horizontal and vertical approaches. We now included a more detailed description in the Methods section (from line 894) as well as in the main text (lines 393-400).Aschenbrenner et al., Genome Medicine, 2021, integrates CoCena networks calculating the correlation between identical features (genes) in distinct datasets, making it the ideal tool for integrating different layers of the same omic type. To integrate multi-omics datasets, we further developed and report here vCoCena. A more extended description is provided in Results and Methods sections (from line 894).Briefly, vCoCena and hCoCena are extensions of the original CoCena pipeline for co-expression network analysis aiming at integrating multiple layers of data in one network. hCoCena, first reported in Were the tests corrected for multiple testing?We apologize, if this was not sufficiently described. We specified corrected p values and the applied correction method wherever appropriate throughout the manuscript. To make this more visible, we included a small note in the main text , besides having it further specified in the Methods section and figure legends . As specifically asked by Reviewer 3, q values for GSEA on the whole GO dataset are also available in Supplementary files for each biological term. Multiple testing was not applied to FGSEA on gene expression log2FC ranks, being these enrichments inspected on selected GO terms according to the results on the upstream analysis.3) Application aspect: What about its prediction accuracy within an individual cancer subtype? Will it achieve high prediction accuracy on intra-tumor type heterogeneity?We thank the reviewers for this interesting question. We now tested the prediction on individual cancer subtypes separately, showing a robust predictive potential .Can the algorithm be applied to other drugs in this same class?To address this extremely important point, that we agree with the reviewer would be key to prove the versatility of our approach, we further applied the pipeline to the prediction of cancer cell lines’ sensitivity to cisplatin, a thoroughly reported broad-acting chemotherapeutic also acting as a DNA damaging agent. Results strongly supported the broad applicability of our approach, which was able to predict sensitivity to this reference drug with extremely high accuracy.The inclusion of this other compound allowed us to further improve the prediction pipeline: we realized that a more valuable and balanced vertical network construction could be achieved when equilibrating the input of DE and DAR-associated genes from the RNA-seq and ATAC-seq analyses. This threshold was set to the top 1000 most variable DE genes from RNA-seq and the top 1000 most variable DAR-associated genes from ATAC-seq . This allowed to prevent, and this was more evident in the cisplatin dataset, that the ATAC-seq layer could take over the RNAseq one in the final signature construction, just due to the initial much higher number of input entries. Of course, this implied to revise both the CoCena and prediction Figure 5 and 6, with overall analogue findings and an uncompromised prediction goodness. Beyond adjusting the prediction workflow as explained, we also updated the promoter annotation of ATAC-seq peaks to a newer version. This is the reason for the slight changes in Figure 4, which indeed actually improved the overall biological findings.Reviewer #1 (Recommendations for the authors):The crosswise approach is not easily comprehendible. It appears to be a network analysis on the union of genes from RNA-seq and ATAC-seq. I wonder if the introduction can be more clear and state uniqueness compared to existing approaches.We apologize to the reviewer for not including sufficient details in describing the difference between the already existing CoCena network approach and the other two approaches, namely horizontal and vertical approaches. We now included a more detailed description not only in the Methods section (from line 894) but also in the main text as suggested by the reviewer (lines 393-400).Aschenbrenner et al., Genome Medicine, 2021, integrates CoCena networks calculating the correlation between identical features (genes) in distinct datasets, making it the ideal tool for integrating different layers from the same omic technology. To integrate multi-omics datasets, we further developed and report here vCoCena. A more extended description is provided in the Methods section (from line 894).Briefly, vCoCena and hCoCena are extensions of the original CoCena pipeline for co-expression network analysis aiming at integrating multiple layers of data in one network. hCoCena, first reported in Overall, figure quality is high. However, color coding in figures  is unique but not intuitive. Therefore, I had to refer to legends all the way to the last figure. I would suggest changing the color scheme to more intuitively or just use numbers/texts.To make the figures reading more intuitive, we included explicit texts besides color coding for each condition in all figure panels where needed .Reviewer #2 (Recommendations for the authors):Overall I found this manuscript very interesting to read and significant to the field. However, please consider the following questions/comments:1. Authors mentioned that HCT-15 is low-sensitivity vs BxPC-3 is high-sensitivity. Is there any data that illustrates this point? Or any description of the phenotype after treatment?We apologize for not being clear enough when introducing previous findings on the differential sensitivity of HCT-15 and BxPC-3 cancer cell lines to 3-CePs. We now included an introduction to the previous work done by our lab in the revised manuscript showing preferential activity of these agents against the pancreatic cancer cell line in both 2D and 3D cancer models  as well as in the main text (lines 393-400).Aschenbrenner et al., Genome Medicine, 2021, integrates CoCena networks calculating the correlation between identical features (genes) in distinct datasets, making it the ideal tool for integrating different layers of the same omic type. To integrate multi-omics datasets, we further developed and report here vCoCena. A more extended description is provided in Results and Methods sections (from line 894).Briefly, vCoCena and hCoCena are extensions of the original CoCena pipeline for co-expression network analysis aiming at integrating multiple layers of data in one network. hCoCena, first reported in 3. I am also confused about Figure 5B panel 1 on GFC. I thought GFC is a relative fold change compared to treatment to control. What is the control group in this case to calculate GFC?Group Fold Change which was originally described in Aschenbrenner et al., Genome Medicine 2021 as follows: “To assess the expression strength of the found gene clusters in the different studied conditions, the group fold changes (GFCs) of the conditions are calculated for each gene by calculating the mean expression of a gene over all samples and then computing the fold change of the mean gene expression within each condition from the overall mean.” To make this more clear for the reader of the current manuscript, we now included the detailed definition of GFC in the Methods section at line 935.Apologies to the reviewer for not being clear enough. GFC is the 4. The pink module is clearly of interest as it showcases the necessity of this multi-omic approach. More generally, can we label all the modules that are only identifiable cross-wise but not by single omic? Can we show a few more detailed examples here? Why are they identified now but not by the single omics approach?steelblue module was reported to illustrate the cross-wise integration adding novel insights into the mechanism of response (lines 451-464). The now so-called steelblue module has indeed a similar regulation to the previous pink one. We further described in the main text also the other integrated and single-omic modules from vCoCena to further point out the advantage of such network analysis (see lines 427-450).We thank the reviewer for this suggestion. In the revised version of the network, the The overall added value of using vCoCena compared to the initial single layer non-network based analysis consists in the ability to take apart genes with the same regulation pattern, both inside one omic layer and between layers. Grouping transcripts and promoters into the same regulatory module allows to maximize the ratio of hits producing enrichments otherwise not recognizable from separate analysis, due to insufficient representation in the single omics (this is a matter of set size and ratio in this set). Starting both the transcriptome and chromatin horizontal networks from the union of the top 1000 variable genes from both RNA and ATAC-seq datasets, further enhances the multi-omic integration.5. The prediction algorithm at the end shows good prediction power across cancer types. What about its prediction accuracy within an individual cancer subtype? Will it achieve high prediction accuracy on intra-tumor type heterogeneity?We thank the reviewer for this interesting question. We now tested the prediction on individual cancer subtypes separately, showing a robust predictive potential (see figure 6 —figure supplement 2 A).Reviewer #3 (Recommendations for the authors):I enjoyed reading your work and the approach you took to integrating multi-omics data to better understand the mechanism of action and epigenetic signatures of 3-CePs in cancer cell lines. The manuscript was clearly written, and I appreciated the thorough results and methods sections which allowed me to clearly follow the majority of the steps taken.We thank the reviewer for these very positive statements.As for major revisions, I believe it would be valuable if the authors would test their sensitivity model for compound B in addition to compound M in cell lines described on page 12 and in Figure 6D.To further validate the versatility of our platform beyond the specific class of compounds, we further applied the pipeline to the prediction of cancer cell lines’ sensitivity to cisplatin, a thoroughly reported broad-acting chemotherapeutic also acting as a DNA damaging agent. Results strongly supported the broad applicability of our approach, which was able to predict sensitivity to this reference drug with extremely high accuracy.It is great to see your work in this space and I am hopeful that it further strengthens the use of these data types and analysis methods for improving the drug development process. I will include my general thoughts and suggestions below.General ThoughtsThe paper is long – especially the Results section. But I think for this style of work it is better to have a longer, more thoroughly described results and methods section than to leave out key details.We thank the reviewer for the positive comment. We also believe that, given the complexity of specific passages in our workflow, it is better to spend a few words more than to take concepts for granted.I would prefer if the paper included p-values for key findings in the text of the article instead of having to locate them in the supplement or figures. I did not see FDRs or q values mentioned throughout the paper/findings – were these calculated? Was there a reason the authors chose to report p-value<0.05 findings as opposed to an FDR/q-value threshold? I find that with gene set enrichment analyses, it can be important to use FDR.We apologize with the reviewer, if this was not sufficiently described. We specified corrected p values and the applied correction method wherever appropriate throughout the manuscript. To make this more visible, we included a small note in the main text , besides having it further specified in the Methods section and figure legends . q values for GSEA on the whole GO dataset are also available in Supplementary Data tables for each biological term. Multiple testing was not applied to FGSEA on gene expression log2FC ranks, being these enrichments inspected on selected GO terms according to the results on the upstream analysis. We decided to employ GSEA cursorily in different steps of the analysis in order to get a preliminary overview of possible pathways to investigate. The high redundancy observed among significant (p<0.05) terms (see Supplementary file 10) further confirmed the consistency of emerged biological processes.With regards to the crosswise integration method performed, the authors describe a threshold minimum of 15 nodes per cluster on page 23, line 757 of the manuscript. How was this threshold selected? It would be helpful to mention the total sample size input for constructing the network in these sections.This is a very valid point. We now report the hit input size upon revising our strategy also in the Result section (line 411) besides having them specified in the Methods section (line 925). Concerning the 15 nodes threshold, we inspected different values from a minimum of 10 and chose such threshold empirically with the overall goal to maximize the number of detected modules with specific regulation patterns  without compromising the possibility to get efficient functional enrichments.For the sensitivity signature, the authors selected LASSO regression – did the authors consider linear regression? How was LASSO chosen?We thank the reviewer for this comment. In essence, we agree with the reviewer that probably a simple linear regression without parameter shrinking would also be an option. However, we chose to use l1-regularized linear regression (the lasso), because it is an excellent option in high-dimensional settings. We have recently compared many different approaches in a similar but not identical setting  and could show that LASSO led to very robust results when using transcriptome data in a clinical setting. Based on this experience, we used LASSO. We hope that the reviewer can agree with us that we would not extend the work here to introduce additional other regression models for benchmarking since it would further lengthen the manuscript and would somewhat distract from the main message of the manuscript.For the genes included in the perturbation-informed signature , what was the p-value and fold change thresholds used?Based on the question of the reviewer and with the new data about Cisplatin integrated into the manuscript we have further improved this section. The M signature was obtained by intersecting genes coming from the selected vCoCena modules with DE genes from the comparison between HCT-15 and BxPC-3 untreated controls using a min abs(log2FC) = 1 and padj < 0.01 to strictly select the most significant hallmark genes. Updated information on the final signature size after revising (both for M and cisplatin) have now been included also in the main text  besides having it specified in the Methods section .For the sensitivity score (described page 12) – do the authors think that this is the ideal model for calculating a sensitivity score? Did they play around with changing the input for the sensitivity model or the model itself? It would be helpful to understand in greater detail why the authors felt that this was the best input for the model.This is a very interesting question. Considering the promising predictive outcome, we would argue that the score itself was efficient enough to describe sensitivity. To further support this observation, we now further validated it using a well-established DNA damaging anticancer drug, cisplatin. As we see the experimental validation to be the strongest support for the usefulness of the model, we opted not to further explore the model itself at this time. But we would like to thank the reviewer for this excellent idea and we definitely will continue on the modeling part in a follow-up more computational project to see whether the model itself can be further improved without losing the simplicity of the selected score type. One could also think about extending the model in future developments to scRNA-seq and scATAC-seq data as input. However, we feel that this would be beyond the current scope of the manuscript and we hope the reviewer can agree with this approach.The validation of the sensitivity prediction on cancer cell lines is given a short, one-sentence description in the results. This could be better explained.We thank the reviewer for this comment and we addressed this aspect now in the text as suggested at lines 516-521.In the discussion and in other locations in the text, the authors describe their approach as 'efficient and versatile' which I think generally ignores the fact that this required next-generation sequencing, two assay types  and in-depth systems biology approaches. Is the argument that the network model is now versatile in that it can be applied to other drugs in this same class? Or that the approach can be borrowed by other groups for other classes of drugs and sample types?Again, these are very valid points by the reviewer. As outlined earlier (see above), we are convinced that – while currently still in its infancy and novel – multi-omics approaches together with in-depth systems biology approaches will revolutionize biology and medicine and will guide the way towards precision medicine . However, we agree with the reviewer that we should be more explicit and outspoken. Therefore, to address this extremely important point, and to further illustrate versatility of the approach, we included another well-known drug, cisplatin , illustrating how efficient our approach predicts cancer cell lines’ sensitivity to cisplatin. We therefore are convinced that the proposed approach is both versatile in being applicable to other drugs and, as a consequence, generalizable to other diseases and target cell populations. We made changes accordingly in lines 695-697.In the methods section, there were a few details I would like to see added. Product numbers including the ATCC product #s, propidium iodide product #, RNA tape station assay. EDTA concentration in ATAC-seq. For clarity, I recommend breaking the RNAseq and ATACseq methods sections apart. Please also include the sample numbers per group that were sequenced. This was hard to find.We added these details as suggested by the reviewer in the Methods section .


The incidence and mortality from prostate cancer (PCa) are on the rise which poses a major public health concern worldwide. In this narrative review, we have summarized the characteristics of major in vitro and in vivo PCa models including their utility in developing treatment strategies. Androgens, particularly, testosterone and dihydrotestosterone (DHT) activate the androgen receptor (AR) signaling pathway that facilitates the development and progression of castration resistant PCa. Several enzymes namely, CYP17A1, HSD17B, and SRD5A are essential to furnishing DHT from dehydroepiandrosterone in the classical pathway while DHT is formed from androstanediol in the backdoor pathway. The advancement in delineating the molecular heterogeneity of PCa has been possible through the development of several in vitro and in vivo research models. Generally, tissue culture models are advantageous to understand PCa biology and investigate the efficacy and toxicity of novel agents; nevertheless, animal models are indispensable to studying the PCa etiology and treatment since they can simulate the tumor microenvironment that plays a central role in initiation and progression of the disease. Moreover, the availability of several genetically engineered mouse models has made it possible to study the metastasis process. However, the conventional models are not devoid of limitations. For example, the lack of heterogeneity in tissue culture models and the variation of metastatic characteristics in xenograft models are obviously challenging. Additionally, due to the racial and ethnic disparities in PCa pathophysiology, a new model that can represent PCa encompassing different ethnicities is urgently needed. New models should continue to evolve to address the genetic and molecular complexities as well as to further elucidate the finer details of the steroidogenic pathway associated with PCa. Prostate cancer (PCa) is a frequently diagnosed malignancy that ranks second in cancer-related deaths in men in the United States . The groAR usually displays preferential binding to heat shock proteins (HSP) in its dormant form . Upon biSeveral PCa cell lines were developed in the early 1970s and 1980s, such as LNCaP, DU145, and PC-3 which are still indispensable in research ,21. NeveThe androgen responsive LNCaP cell line possesses the mRNA/protein expression of both AR and prostate specific antigen (PSA). This cell line was established from lymph node metastasis . In the PC-3 cells that do not exhibit AR or PSA mRNA/protein were obtained from vertebral metastasis of prostate tumor. Their doubling time is about 33 h. This cell line expresses aberrant p53 with a nonsense codon that demonstrates a loss of heterozygosity ,36. TheyLNCaP C4-2B subline is androgen independent and has a doubling time of 48 h. These cells possess a greater metastatic potential than the parental LNCaP cell line. When these cells are injected intraosseous or intracardially into the immunodeficient mice, they produce osteoblastic or mixed osteolytic–osteoblastic lesions. Due to the elimination of different molecular properties of localized and treatment-naive PCa, this cell line attains the most undifferentiated state of PCa . LNCaP aLAPC-4 cell line was generated from the lymph node metastasis of a hormone-refractory PCa patient through sequential subcutaneous xenografting into SCID mice. While developing this cell line, the explants from the PCa patients were xenografted into mice from which the tumor cells were later grown in culture dishes . The douThe VCaP cell line was generated from a hormone-refractory metastatic PCa patient . This ceThe RWPE-1 cell line was obtained from a 54-year-old man’s prostate and human papillomavirus 18 (HPV-18) was used to make the cell immortalized . Their dLike most other cancers, the development of drug resistance is frequently observed in PCa. Therefore, drug-resistant cell lines can be employed in research to identify novel molecular mechanisms of chemoresistance. Mohr et al. reported the generation of docetaxel-resistant PCa cell lines. In their study, they initially treated DU145 and 22Rv1 cells with 10 nM of docetaxel followed by an increase in drug concentrations . They confirmed docetaxel resistance by colony formation assay and phenotypic characterization by qRT-PCR . Hongo eLNCaP cell line is very commonly used for developing mouse xenografts. This cell line was isolated from lymph node metastasis . After iThe PC-3 cell line can also be used for developing mouse xenografts. Stephenson et al. reported the formation of lymph node metastases through orthotopic implantation of PC-3 cells . PC-3M iThe DU145 cell line is another variant that is also used for developing xenografts. Both PC-3 and DU145 cells are androgen-independent and they do not express AR. Culig et al. created an LNCaP-abl cell line after culturing LNCaP cells 87 times in a medium devoid of androgen. These cells exhibit a greater amount of AR and display an amplified proliferation to androgen until passage 75. In nude mice, bicalutamide promoted the growth of LNCaP-abl xenografts while testosterone suppressed it . LNCaP-IAlthough cell derived xenograft models are useful to assess the efficacy and mechanisms of drugs, they are devoid of heterogeneity and cannot mimic the complexity of the tumor microenvironment. Hence, PDX models are advantageous that are developed by relocating patient tumor samples into host animals. These models can better replicate tumor heterogeneity which is helpful to design more effective therapeutics. In 1976, Schroeder et al. first reported the transplantation of PCa tissues into nude mice . Despite+/− knockout leads to multifarious lesions such as endometrial complex atypical hyperplasia, dysplastic intestinal polyps, lymphomas, and PIN [The deletion of phosphatase, PTEN takes place about in 86% CRPC . While t and PIN ,72. This and PIN . pc−/− Smad4pc−/− mice that were later used to study the effects of combination therapy of a hypoxia-prodrug, TH-302, and checkpoint blocker. This combination therapy markedly extended the survival of PTENpc−/− Smad4pc−/− mice [PTENadcre+) mouse model where they generated prostate-specific Cre-LoxP genes through the delivery of adenovirus to the anterior prostate lobes. This model was a significant milestone to compare the age difference in PCa since the result showed that the old mice group has a greater frequency of PCa development and progression compared to young mice [Ding et al. generated PTEN−/− mice . Further−/− mice . Liu et ung mice . Table 3The transgenic adenocarcinoma of the mouse prostate (TRAMP) model was developed by Norman Greenberg and his group in 1995 . In thisAndrogen receptor knockout (ARKO) models are outstanding models since they are useful to study AR physiological roles in selective cell types within reproductive systems. To develop the ARKO mice, C57-B6/129/SvEv loxP-floxed AR mice were first generated and then they were mated with Cre recombinase expressing mice ,85. FloxThe PCa inflammation models are heavily impacted by carcinogens, animal genetics, hormonal imbalance, fat-enriched diet, cholesterol, and advanced age. For example, an elevated expression of estrogen receptor-α (ER-α) and a decreased expression of ER-β and androgen receptor (AR) promoted the oncogenic effect of 17β-estradiol on NRP-152 cells . ChronicThe causative factors for prostatic infection are usually bacteria, fungi, uric acid crystals or urine reflux. All these stimuli result in the activation of proinflammatory cytokines in the prostate that promote tumor development. Reports suggest that an elevated expression of inflammasome complex in the prostate of a rat model may mimic an inflammatory state similar to BPH in humans . ElkahwaClinical models of PCa research are primarily limited to human prostate tissues and PCa patients. Though preclinical models offer the advantage of ready availability and flexibility of treatment, clinical models provide the targeted endpoint of the research in question. Prostate tissues from healthy humans or patients represent a unique but challenging opportunity to understand the disease pathogenesis and key players that are involved in the disease. Higher expression of steroidogenic enzymes in human prostate homogenates (HPH) compared to cell lines offers significant advantages in terms of experimental ease and clinical relevance. Typically, human prostate tissues are homogenized which are termed HPH in laboratory terminology. Based on the maturity pattern, histology, and activity, the human prostate gland can be classified into several zones such as fibromuscular, transition, central, and peripheral ,99. ClinDe novo steroidogenesis in the tissues reveals the intratumoral dynamics in CRPC ,103. DetIt is undebatable that PCa patients in clinical studies are the gold standard of research in this area. The species differences in metabolic landscape and tumor microenvironment between rodents and humans can pose a discrepancy in outcome . While iTill today, a perfect model for PCa does not exist and this makes the research somehow ineffectual on a lot of occasions. Since there are legitimate ethical issues in relation to research on humans, the use of a mouse is still the choice of model. It is, indeed, imperative to find out the underlying cellular and molecular mechanisms of PCa development, progression, metastasis, and drug resistance to devise more effective therapeutic strategies. Despite being powerful, GEMMs are barely used to determine the effects of drugs. This is primarily due to their lengthy time course . SimilarThe use of PCa cells in cancer stem cell (CSC) research has also been reported that introduced a new horizon in PCa research. PCa stem cells were identified by Collins et al. from primary human PCa . These CThe currently available PCa cell line models have several limitations. For example, these cell lines are devoid of heterogeneity that is frequently detected in patient-derived tumor samples . Indeed,Compared to cell line models, patient-derived xenografts (PDXs) are preferred since they are grown in immunocompromised mice that can more precisely mimic the heterogeneity of human PCa cells. Additionally, they have their endocrine system unaffected, and they can be used in metastatic conditions. In the earlier days, the success with PDX was very limited; however, presently a good number of PDX models are being employed globally for evaluating the effects of different chemotherapeutics with enhanced success . HoweverConsidering all these downsides, three-dimensional (3-D) cultures have emerged as a novel and suitable alternative to patient-derived cancer models. For example, spheroid culture model is created by using culture plates with low attachment properties and they offer substantial success rates . AnotherThe availability of a number of models has remarkably advanced the field of PCa research. Although the in vitro models offer convenience to study cancer-related pathways, in vivo models are useful to better understand the different steps of tumor progression. Nevertheless, the cell line models have a significant drawback of not producing the precise human tumor microenvironment and that is why in vivo models are of paramount importance since PDX in mice can better mimic the human tumor environment. Moreover, GEMs provide with a huge advantage of developing different types of PCa tumors. However, a model that can represent PCa of multifarious ethnicity is still lacking and hence, further investigation is unquestionably necessary to better understand the oncological drivers of PCa as well as to devise more effective therapeutic strategies.