Esophageal cancer remains one of the hardest cancers to treat with rising incidence rates, low overall survival and high levels of treatment resistance. The lack of clinically available biomarkers hinder diagnosis and treatment stratification. While large scale sequencing approaches have uncovered a number of molecular makers, little has translated in the routine treatment of esophageal cancer patients.We evaluate the treatment response towards a panel of 215 FDA-approved and 163 epigenetic compounds of 4 established and 2 patient-derived esophageal cancer cell lines. Cell viability was evaluated after 72h of treatment using cell titer glow. The drug sensitivity testing results for gemcitabine and cisplatin were validated using clonogenic assays.The tested cell lines display different drug sensitivity profiles, although we found compounds that display efficacy in all of the tested established or patient-derived cell lines. Clonogenic assays confirmed the validity of the drug sensitivity testing results. Using the epigenetic library, we observed high sensitivity towards a number of epigenetic modifiers.Ex vivo drug sensitivity testing may present a viable option for the treatment stratification of esophageal cancer patients and holds the potential to greatly improve patient outcome while reducing treatment toxicity. Apart fA number of recent high-throughput investigations revealed histological, genetic and epigenetic changes typical for esophageal adenocarcinoma . While, Precision medicine approaches for the treatment stratification of patients with esophageal adenocarcinoma have recently come into the focus of the field in order to improve patient outcome. Apart from improvements in PET imaging techniques , 12, theWe have overcome these issues by adapting a drug sensitivity testing (DST) platform previously established for the stratification of patients with relapsed/refractory AML . Ex vivoOE33, OE19 and SKGT2 human esophageal adenocarcinoma cell lines were obtained from the European Collection of Cell Culture (ATCC). Flo1, EAC42 and EAC47 cells were established in the Capobianco Laboratory. EAC42 and EAC47 human esophageal adenocarcinoma cell lines are derived from patients undergoing surgery at the Miller School of Medicine, University of Miami . The CapThe normal esophageal epithelium cell line EACN42 was established in the Capobianco laboratory. The cell lines was derived from human normal epithelium and immortalized using HPV E6-E7.mod) function developed by Swords et al [mod (sDSSmod) for each drug in each patient screen is calculated according to the formula sDSSmod = DSSmod (patient cells)—DSSmod . Given in this way, the sDSSmod incorporated information on each drug’s potency, efficacy, effect range and therapeutic index, making it possible to prioritize compounds over multiple parameters using a single numerical metric. In addition, this methodology allows us to rank compounds by cancer-selective efficacy for each individual patient. For example, a large positive sDSSmod means that a compound is highly selective for esophageal cancer cells over normal cells in a given sample (favorable scenario), while a large negative score means that the effect was preferential for normal cells (unfavorable scenario). All calculations and scoring routines are implemented in MatLab and additional curve fitting and statistical analyses are performed in GraphPad prism.Drug sensitivity testing (DST) was performed as described previously . Brieflyds et al . As a fiClonogenic assays were performed as described previously . BrieflyWe tested a 163 compound epigenetic library that consists of inhibitors and activators of epigenetic modifying enzymes . All compounds were dissolved in 100% DMSO and tested in duplicate at a nominal test concentration of 6μM. Wells with assay buffer containing DMSO served as negative controls. 1000 exponentially growing cells were seeded per well in 384-well micro-titer plates and incubated in the presence of compounds in a humidified environment at 37°C and 5% CO2. After 72 hours of treatment, cell viability was assessed by measuring ATP levels via bioluminescence . Positive hits were defined as any compound that showed cell killing higher that 3 standard deviations of the negative control. Curve fitting and statistical analyses are performed in GraphPad prism.The normal esophageal epithelium cell line EACN42 was exposed to the 215 compound FDA-approved library cell viability was evaluated after 72 of treatment. The esophageal epithelial cells display toxicity to 71 compounds . SignifiBecause matched normal tissue is not available at sufficient quantities from patients with esophageal adenocarcinoma from either biopsies or surgical intervention, we compared the response of the esophageal epithelium to the treatment response of white blood cells derived from a healthy donor . While wDrug sensitivity of 4 established and 2 low passage patient-derived esophageal cancer cell lines towards a panel of 215 FDA-approved compounds was established using the DST platform.Similar to what we have previously observed in AML , the 4 tIn addition to the established esophageal cancer cell lines, we evaluated EAC42 and EAC47, two patient-derived esophageal cell lines at low passages. Although both lines differ in the magnitude of response, the drug sensitivity profiles are very similar Figs  and 3. Trd and 6th treatment suggestion in EAC42 and EAC47, respectively  was the 3ectively . Cisplatin EAC47 . Treatmein EAC47 .OE33, Flo-1 and EAC47 cells were exposed to a 163 compound library containing epigenetic modifiers at a concentration of 6μM. Positive hits were defined as any compound that showed cell killing higher that 3 standard deviations of the negative control.17 compounds displayed a significant reduction in cell viability in OE33, the majority of which were HDAC inhibitors . Flo-1 a10 compounds displayed a significant reduction of cell survival in all of the tested cell lines . With thEx vivo DST screening may present a new stratification approach for patients with esophageal adenocarcinoma that allows the identification of patient-specific treatments while reducing normal tissue toxicity [Treatment stratification of patients with esophageal adenocarcinoma is problematic due to the lack of molecular markers predicting outcome and treatment response, and the optimal patient management remains controversial. While the collection of large sequencing data sets revealed genetic changes, including alterations in DNA methylation patterns , 6–8, thtoxicity .The esophageal adenocarcinoma cell lines display a wide range of treatment responses and vastly different individual sensitivity profiles. In contrast to our previous observations in AML, we uncovered a small subset of compounds that displayed treatment efficacy in all of the tested samples. Specifically, Bortezomib displayed high activity in all lines. The DST screen revealed a number of treatment options not part of standard therapy in esophageal adenocarcinoma, while compounds commonly used in patients such as platinum drugs , taxanes , 5-FU, irinotecan, capecitabine and epirubicin display treatment efficacy in few or none of the lines. However, similar to previous observations, a treatment response can be observed in response to treatment with cisplatin when doses are escalated beyond the maximum dose of the DST screen. Due to high levels of normal tissue toxicity and numerous difficult to manage side effects, a similar dose escalation is not feasible in a clinical setting. The dose range used on the DST screen was chosen to represent a clinically relevant treatment dose. Therefore, a negative result on the DST screen does not imply a complete lack of treatment response. Nevertheless, as seen in the case of cisplatin treatment, the dose needed for a clinically relevant response lies outside of the dose range that is clinically feasible. The data derived from the FDA screen suggests that phenotypic-based treatment stratification may present a way to overcome the current issues with patient stratification and classification.The impact of epigenetic modifications has recently come into the focus of the esophageal adenocarcinoma community and studies have shown numerous changes in epigenetic marks in patient samples. A small number of studies have provided encouraging results for the use of HDAC inhibitors for the treatment of esophageal cancer –20. We oA pilot trial evaluating a set of patients with esophageal adenocarcinoma will be necessary to examine the clinical feasibility of this precision medicine approach. Clinically the treatment of esophageal adenocarcinoma consist of a multimodal approach. Although our platform evaluates the response towards single treatments only, our experience using the DST platform for the treatment stratification of patients with relapsed/refractory AML  suggestsex vivo screen of this magnitude. This can be overcome by the use of surgical specimen which yield sufficient cellular material to support the DST screen. Although a matched normal tissue sample represents the ideal toxicity control for the DST screen, normal esophageal epithelium is not obtainable from either biopsies or surgical samples. White blood cells, while a matched control, display a vastly different sensitivity profile than the esophageal epithelium and cannot be used to evaluate epithelial toxicity in the esophagus. This can be overcome by using a catchment area matched panel of normal esophageal epithelium samples as a normal reference for future patient screens.Although all patients receive at least one biopsy, the material obtained during the procedure is essential for diagnostic purposes and the material superfluous to pathological staging is rarely of sufficient quantity and quality to support and This precision medicine approach has the potential to improve treatment stratification and outcome for patients with esophageal adenocarcinoma. Additionally, DST screens on patient populations may allow us further insight into the biology of the disease through the accumulation of sensitivity information on different classes of therapeutic compounds.S1 FigBar graphs of clinically actionable drug responses for (A) Flo1 and (B) SK-GT2.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


The first ideas leading to The Two‐Signal Model of lymphocyte activation were published 50 years ago, but the model was not realized in one sitting. I describe the three phases that led to its contemporary formulations. A motivation underlying all these models was to generate a minimal description of what is required for antigen to inactivate and activate mature lymphocytes that, at the same time, accounts for how peripheral tolerance is achieved. I suggest the two signal model has not only provided a substantiated framework for understanding how antigen interacts differently with B cells and CD8 T cells, to result in their inactivation and activation, but its postulates are pertinent to contemporary issues concerning the inactivation and activation of CD4 T cells. The most obvious and prosaic is to give credit where credit is due. The second is more important and interesting. Knowledge of how important advances are made is germane to understanding how progress occurs. Such understanding can be empowering.I clarify at the outset what different people mean by “two signal models” in the context of lymphocyte activation. There are two different and sometimes confusing uses. Dresser and Mitchison clearly stated in 1968 the idea that competent lymphocytes can interact with antigen in two ways, one way leading to their “paralysis”, or inactivation, and the other leading to their “induction” or activation.2This account was stimulated in part by Cohn's accounts of how the two‐signal model of lymphocyte activation came about. I think these accounts are inaccurate in some respects and so obscure the considerations underlying the initial proposal. They therefore undermine an understanding of the imaginative, analytical and observational considerations involved. A correct appreciation of these considerations might stimulate others to be brave, a quality that fosters creativity. My intellectual braveness, such as it is, has been fostered by appreciating that of others; an appreciation gained by closely following their intellectual journeys. This fostering led me to believe in the subtlety of argument. I felt I was part of a community that recognized sophisticated thought. I hope a correct account of the origin of the two‐signal model can help others to be brave and thus contribute to our culture. The most effective type of braveness is probably unselfconscious; it arises from intense engagement with a problem and a faith, nurtured by experience, that subtle considerations can yield enormous dividends.Cohn wrote an invited article of reminiscences for the 1994 Annual Review of Immunology, entitled the Wisdom of Hindsight.My primary aim, for most of my career, has been to do the best science I can, anticipating this would speak for itself. I found support for this course with my happiness at how Cohn and I had expressed our vision in the 1970 Science article,I started a year of administrative leave in June 2013. I reconsidered my priorities, particularly in view of my age of 70, and my frustration, over decades, that ideas, that I considered may be valuable to the field, were barely considered by immunologists. Some were pertinent to the two‐signal model, others to immune class regulation. I wrote two “Discussion Forums”,The first, on the inactivation/activation of CD4 T cells, drew a long response from Cohn that was published.Peter Bretscher joined my laboratory in 1967; he was, by way of background, an X‐ray crystallographer who had a passionate curiosity about immunology as well as a clear, crisp way of analysing complex problems. This made the difficult question of the self‐nonself discrimination an ideal one to answer; we were aware that it had to be analysed correctly if a next step was to be made in immunology.“We had two prior formulations, those of LederbergCohn, in his 1994 “The Wisdom of Hindsight” article,I now describe my recollections concerning the origins of the two‐signal model of lymphocyte activation. I apologize for going into considerable detail; such an exposition seems necessary to both understand the basis, and assess the legitimacy, of what I say. In addition, this model was not developed at a single sitting. Indeed, the formulations given by Cohn and myself started going in different directions, perhaps not unnaturally, when I left Mel's laboratory in 1972, as I shall indicate.2.1I became interested in immunology as a graduate student, while doing research in protein X‐ray crystallography, at the UK Cambridge Laboratory of Molecular Biology. I started graduate studies in 1964. I was much more interested, from the beginning, in what was going on in the Division of Molecular Genetics than in that of Structural Studies, to which I belonged. Brenner and Crick jointly headed the former division. About five graduate students and postdoctoral fellows, primarily from this division, met regularly on Saturday mornings for a few hours to chat, with Sydney Brenner and over Nescafe, about diverse scientific topics. I joined my brother Mark, on becoming a graduate student, in attending these get‐togethers. Mark soon left for postdoctoral studies. I continued attending these get‐togethers.One Saturday morning, in 1966, Sydney talked about a paper by himself and Cesar Milstein, on a model for “The origin of antibody variation”, just accepted by Nature.In time, I made use of the invitation, placed on a noticeboard outside Francis Crick's office, to discuss science. I knocked on Crick's door on at least ten occasions. One of my earliest immunological ideas is now obvious. However, at the time, Francis appeared to think it new and was kindly enthusiastic. Some reasonable people were then unsure about the plausibility of the Clonal Selection Theory. My ideas buttressed the plausibility of some of the critical features of this theory.uniquely only activate the transcription of genes coding for the antibody receptor that had interacted with antigen. I was thus led to the proposition one cell “must” be committed to the synthesis of antibody of just one specificity. These considerations were obviously another way of making plausible some essential aspects of the Clonal Selection Theory. They formed part of the two papers Cohn and I subsequently published in Nature in 1968I supposed that, when antigen interacted with antibody receptors on an antibody precursor cell, there must be “an interaction sensing site” that “recognizes” this interaction via changes in the structure/accessibility of the antibody receptor. I considered the situation that would occur as the genes coding for immunoglobulin chains are generated, in evolutionary time or somatically. I considered that this “interaction sensing unit” must be complementary to an invariant part of the antibody molecule; if it was complementary to a variable part, there would have to be two “complementary” mutations, one in the antibody genes, the other in the gene encoding the interacting sensing unit, an unlikely event. Assume, as seemed plausible, that the “interaction sensing unit” was complementary to an invariant part of the antibody. In this case, if the cell had two chemically different antibody receptors, the cell could not distinguish which of the antibodies interacted with antigen, and so could not, for example, Crick encouraged me, once he understood what I was driving at. Francis could initially not understand what I was saying, given my nervousness. He recommended I put my ideas on one side of a page. I gave him my effort. The argument was dense, and as rigorous as I could make it. He in time understood my considerations. I remember the details of this interaction, as it was so important to me. Francis chastised me, in his jovial way, a style he employed to lessen the sharpness of his advice. I did not need to take his recommendation of one page so literally, and so make the argument so dense. I did not have to prove my proposal, just make it plausible. My interactions with Francis determined to a considerable extent my scientific fate.On getting interested in immunology, I asked Cesar Milstein for advice on what to read. He suggested Burnet and lent me his copy of The Clonal Selection Theory of Acquired Immunity.3This accident occurred in mid‐1966. I was using a chemical that reacted strongly with proteins and readily permeated skin. I wore gloves when using it. The tip of my right index finger started throbbing one night as I fell asleep, on a day I had used this chemical. The next morning the tip was white and quite without feeling. In the long run, the tip was surgically removed and skin from my arm was grafted onto the raw end. However, before this, during the days after the mishap, a sharp line developed between what I imagined was healthy me and the proteins of my finger tip, altered by reacting with this chemical and so becoming “foreign”. This personal experience stimulated my interest in how the immune system managed to respond to foreign but not to self‐antigens. My limited reading, or a conversation with Cesar Milstein, I now do not know which, led me to believe that antigen could interact with antibody precursor cells in two ways, one leading to the antibody precursor cell's inactivation, the other to its activation, that is, multiplication and the differentiation of its progeny into antibody‐secreting cells.I had already decided to try to become an immunologist if I managed to get my PhD. In talking about this with Francis, he suggested I might do postdoctoral studies with Avrio n Mitchison, at that time at Mill Hill, London. I therefore visited Av, as he was known to all, probably sometime in 1966. Av explained his laboratory was very full but that, if I really wanted to come, he could probably squeeze me in.4I had some ideas, probably in 1967, and certainly after my meeting with Av, about how antigen interacts differently with antibody precursor cells to result in their activation and inactivation. I realized there were three appealing characteristics of the proposal that single precursor cells are inactivated by antigen, but that their activation requires the antigen‐mediated interaction between specific lymphocytes. Firstly, it was the only proposal I knew of that attempted to account for how antigen interacted differently with the same antibody precursor cell to result in either its inactivation or its activation. Secondly, it “explained” why antigens had to be macromolecular to be immunogenic. A small, univalent molecule, could not mediate the interaction between lymphocytes. Thirdly, it accounted for how self‐nonself discrimination could be achieved, as outlined elsewhere.immunogenic carrier, namely bovine serum albumin (BSA).I searched the literature, once I had formulated the one lymphocyte/multiple lymphocyte model, for observations bearing on it. I had no idea what journals to look at, so I randomly leafed through various periodicals on the library shelves. I found a paper in the Journal of Experimental Medicine that seemed to strongly support the proposal I envisaged. This was a paper from Benacerraf's group on “non‐responder” guinea pigs that did not produce antibody upon immunization with the antigen, poly‐L‐lysine (PLL). These animals produced anti‐PLL antibody when immunized with PLL coupled to an 5Two articles were very important to the further development of my ideas as a graduate student. Firstly, the articleI also came across an article by Gell and Kelus, entitled “Anti‐Antibodies”, in Advances in Immunology.I kept Francis informed on how my immunological ideas progressed. He changed his advice on where to do my postdoc. Francis was now annually visiting the Salk Institute, in southern California, as a Non‐Resident Fellow. Given his appreciation of my interest in theory, he suggested I might do a postdoc with Mel Cohn at the Salk Institute. Naturally, I accepted Francis' advice.Mel Cohn also accepted me on Francis' recommendation. Cohn had a major, NIH‐sponsored, programme for producing myeloma tumours in BALB/c mice. He already had a substantial collection. I was encouraged to apply for a Damon Runyon Postdoctoral Fellowship while a graduate student in Cambridge. I had to outline a plausible research proposal. I proposed testing the hypothesis that myeloma proteins have an abnormal structure, reflecting a structure representative of antibody complexed with antigen. I hypothesized that if such abnormal “myeloma antibodies” were generated, in an antibody precursor cell, the cells might behave as continually activated, and so constitute a cancer of this cell. The prediction was that myeloma proteins, not complexed with antigen, would react with antibodies that normally only recognize antibody sites present on antibodies present in antigen/antibody complexes. Although this idea was not experimentally explored, as I came to feel it implausible once I had been in Mel's laboratory for a few months, it was part of my successful application for a postdoctoral fellowship.Before leaving Cambridge for the Salk Institute, I submitted a letter6My impression, on arriving at the Salk Institute, was that Cohn was primarily occupied with the problem of the origin of the diversity of antibody genes. He was less aware of some of the literature on the requirements to generate antibody responses and generate unresponsive states than I was. I in turn had not come across, as a graduate student, Lederberg's classic Science paper of 1959.I was very motivated, on arriving in Cohn's laboratory, to review the literature; to see how my ideas stood up to current findings and other people's conjectures, and to explore how they might be further developed before “going public”. This was not to be.Mel told me, shortly after my arrival, that he had been invited to a small meeting of about 40 individuals, on Immunological Tolerance, to be shortly held at Brook Lodge, Michigan. Moreover, Mel was asked to summarize the meeting. The invited participants included many leading investigators, including Av Mitchison, Klaus Rajewsky, Bill Weigle and Baruj Benacerraf. The meeting was held from September 18 to September 20, 1968, surely <2 months after I arrived in Mel's laboratory. It was natural for Cohn, in his summary of the meeting, to try to account for major findings in terms of the ideas we were developing.argued that the evidence showed there were also carrier effects in generating unresponsiveness. Cohn, in his recounting the origin of our ideas,Cohn's description of some reported observations was challenged by other attendees at the meeting, as evident from the original transcript, a copy of which Mel gave me. In particular, Cohn was unclear about the nature of Weigle's observations that showed that immunization with an antigen, that crossreacts with the tolerogen, could break the unresponsive state.Both Cohn and I were uneasy about how Cohn had presented our ideas at Brook Lodge. Moreover, the proceedings of the meeting were to be published as a book.non‐immunogenic molecules were also non‐tolerogenic. We argued that such observations were likely misinterpreted, as quantitative considerationstolerogenic but not immunogenic, in accordance with the one lymphocyte/multiple lymphocyte model.I received a personal letter from Av Mitchison, shortly after our Nature paper was published. He said “I think you deeply cloud the issue by using the carrier effect to account for the immunity/tolerance decision. I do not at all see why one cannot have all sorts of lovely carrier effects and also something else, for example, non‐specific stimulation from macrophages” (to induce an antibody precursor cell). Klaus Rajewsky had come to the Salk Institute straight from the Brook Lodge Meeting to talk science, a decision taken on the spur of the moment, as I understood at the time. I recall Klaus' uneasiness about the one lymphocyte/multiple lymphocyte model. As clear from our Nature and Science papers, we disagreed with a conclusion Klaus had come to in his studies in rabbits with porcine LDH. His observations were a particularly clear example of those employed by immunologists to support the proposition that This description gives my best recollection of the events leading up to the publication of our Nature paper, representing the first phase in the development of the Two Signal Model.Our paper was submitted to Nature on October 17, 1968, less than a month after the Brook Lodge Meeting. It was published in the November 2, 1968, issue. I had been in Cohn's laboratory for <4 months, probably closer to two. Our Nature paper pretty well reflected the ideas I brought with me, except that there were references to the ideas of Mitchison, Rajewsky and Jerne on cell cooperation in the generation of antibody responses, and to Rajewsky's observations on tolerogenicity, referred to above. We (meaning I) thanked Crick for discussions related to this paper. Cohn was 46 and I was 25.Cohn said, in his 1994 Annual Reviews article,I have never met Donald Forsdyke, but he emailed me in 2002 over some matter. When he emailed me, I felt I had to let him know of my ignorance of his 1968 Lancet paper when we formulated our ideas.6.1I think it helpful if I explain at this point the three distinct phases in the history of the Two Signal Model of Lymphocyte Activation, as I perceive it. The first phase was the one outlined above, that culminated in our November 1968 Nature paper.6.2The 1968 Nature paper was written fast and under considerable pressure. We were very soon uneasy with part of its content. I consider its strengths to be its articulation that antibody precursor cells would be inactivated upon interacting with antigen alone, and that their activation required a second site be recognized by antigen‐specific “carrier antibody”, derived from or present on another antigen‐specific cell, that is, a lymphocyte. However, its weakness was the envisaged mechanism by which the activation of the antibody precursor cell was “made to depend” on the recognition of a second site on the antigen by the carrier antibody. We suggested that the divalent carrier antibody held the antigen in a way that, when the antibody receptor of the antibody precursor cell bound to two bound antigen molecules, the receptor took on a “distorted” conformation, recognized by an “interaction recognition unit” of the cell. This proposal reflected ideas in my research proposal for a Postdoctoral Fellowship, written when I was in Cambridge. The model predicted that self‐antigens, with repeating epitopes that could distort the antibody receptors in the appropriate way on interacting with them, would induce autoantibodies, an awkward and implausible conclusion. The recognition of this weakness led to the second phase.The “Miller papers”,These circumstances set the scene for the second phase. I was now a Postdoctoral Fellow in Mel's laboratory. Mel and I talked science an average of at least an hour a day. I have never had this type of interaction with anyone before or since. We talked much more broadly than on lymphocyte activation. We were invited in 1970 to give an exposition of our views in Science, an article that went through more than 20 drafts. The articleFirstly, we adhered to the proposition that lymphocyte cooperation was essential for lymphocyte activation and that antigen would, if at a sufficient concentration, inactivate single lymphocytes. We had no idea as to what the signals involved might be in chemical terms, so we formulated an abstract symbolism to express our thoughts. We proposed that when antigen interacts with a lymphocyte's antigen‐specific receptor a signal, signal 1, is generated that, when generated alone for a sustained time, leads to the inactivation of the lymphocyte. The activation of a responder lymphocyte, such as a B cell, also requires its antigen‐specific receptor to bind to antigen, also resulting in the generation of signal 1; its activation required the activity of helper lymphocytes that recognize the same antigen. There was no indication at this time of the MHC‐restricted recognition of antigen by T cells. The T cell receptor was envisaged to be an antibody‐like molecule. In order to make the recognition of antigen by the helper lymphocyte mandatory for the activation of the antibody precursor lymphocyte, we proposed that this recognition by the helper lymphocyte resulted in the initiation of the generation and the delivery of a second signal, signal 2, to the responder lymphocyte. We postulated that this second signal was mandatory for the activation of the responder lymphocyte, a B cell in the case of an antibody response, so that the activation of all lymphocytes was envisaged to be T helper cell‐dependent. Jacques Monod fostered this formulation of our model.Jacques was a Non‐Resident Fellow of the Salk Institute and visited it for a few weeks in the summer of 1969. Jacques was very interested in immunology. He and Mel knew one another well. Mel had done a postdoc with Jacques, working on what became known as the lac operon at an early and critical stage of their research. I very much enjoyed Jacques' scientific company, and I think he enjoyed mine. Mel and I had ideas as to what events might constitute signal 2, as outlined in the next paragraph. Jacques suggested to us the importance, in getting our message over, that we develop a way of expressing the essence of our views, independently of the particular details of how these might be realized. We recognized the wisdom of this suggestion. It was the result of Jacque's prompting that we started to say that the interaction of antigen with the antigen‐specific receptor of the antibody precursor cell resulted in the generation of signal 1, and the recognition of a second site on the antigen, by the T helper cell, or an antigen‐specific molecule it produced, resulted in the generation of signal 2.Our second consideration was directed at how signal 2 might be realized in cellular/molecular terms. We proposed that signal 2 was mediated by short‐range molecules, produced by the helper lymphocyte, similarly as a neuron stimulates or inhibits a cell with which it forms a synapse by the action of neural transmitters it produces, an analogy we employed at the time; alternatively, or in addition, we supposed signal 2 might involve a membrane/membrane interaction between the two interacting cells. These proposals were substantiated once interleukins were discovered and costimulatory systems established as a means by which cells of the immune system communicate. For example, it is now recognized that the activation of B cells requires both the delivery of cytokines to B cellsI should add a comment on the designation of the T cell required to activate a B cell as a helper T cell. MitchisonOur third consideration addressed how the activity of helper T lymphocytes might be controlled. Mitchisonoptimal antigen‐dependent activation of a responding precursor T helper cell required the activity of effector T helpers. This proposal led to the question of how are the first effector T helper cells generated? I call this problem the “priming problem”.The fourth issue was a problem, the existence of which we drew attention to. B cells are precursors of antibody‐secreting cells; their activation results in their proliferation and the differentiation of their progeny into antibody‐secreting cells. This life style of the B cell was envisaged in the formulation of the Clonal Selection Theory, as it explained not only a striking observation, namely the extreme sensitivity of in vivo immune responses to radiation of the animal around the time of immunization, but how very scarce B cells, specific for a particular antigen, could be triggered by antigen to produce substantial antibody after a lag period of a few days. Similar considerations seemed even more critical in the case of the activation of precursor helper T cells, as effector helper T cells have to be generated before B cells can be activated. Recognizing this situation, Cohn and I postulated that precursor T helper cells had to be activated by antigen to multiply and their progeny differentiate to give rise to effector helper T cells. We also proposed that lymphocyte cooperation, presumably between cooperating T cells, was required to activate T cells, thus accounting for how  tolerance is achieved at the T cell level. In more detail, we proposed that the This type of problem is of course not unique to immunology. Life begets life, so how did life originate? Ribosomes contain proteins, and ribosomes are required for protein synthesis, so how were the first proteins produced? We shall see this “priming problem” was important in the third, last phase, of the evolution of two signal models for CD4 T cell activation. Cohn and I suggested different solutions to the priming problem.A final point for clarity is needed. It became apparent from various observations, made after 1970, that there are at least two different processes contributing to self‐tolerance. There is central toleranceI would like to add that my time in Mel's laboratory as a Postdoctoral Fellow, during this second phase, was wonderful. There was a lot of lively, scientific discussion. I could think about anything I wanted to without pressure of who would provide bread the next day. As a graduate student in England I had to count my pennies. The support from my Postdoctoral Fellowships made me feel wealthy. I had the freedom to think about anything I thought interesting and important. I still think my theory of generalized autoimmunity6.3I left Cohn's laboratory in 1972. Cohn and I no longer communicated regularly. It was natural that we worked on different problems and developed different views. I naturally sought and looked out for Cohn's papers. However, I found them often difficult to understand and their contents, when I thought I understood them, often seemed implausible to me. This, on my side, naturally led me to be uncommunicative with Cohn. I believe it would have been natural, if I had liked the ideas that Cohn expressed, that we would have kept in closer contact. Until recently, and after leaving Cohn's laboratory, I only wrote a paper on the two signal model when I had something really new to say, either of a theoreticalCohn responded6.4Studies by Norman KlinmanStudies by LaffertyCharlie Janeway recognized that the presence of helper T cells was required to activate B and CD8 T lymphocytes and that antigen could inactivate these lymphocytes in the absence of helper T cells. He therefore, in 1989, naturally focussed his attention on the circumstances that lead antigen to either inactivate or activate CD4 T helper cells. As is well known, he postulated that the expression of the critical CoS molecule on the APC, needed to generate the second or CoS signal and required to activate CD4 T cells, was only upregulated if a pattern recognition receptor (PRR) of the APC recognizes a pathogen‐associated molecular pattern (PAMP). In the absence of an appropriate PAMP, and consequently inadequate expression of the critical CoS molecules, antigen is envisaged to inactivate the CD4 T cell.Three major and related advances post‐1970 had an impact on our ideas, without undermining them. Firstly, the roles of processing and presentation of antigen by antigen presenting cells (APC) were clarified. Secondly, the fact that nominal antigen is processed and its peptides presented on MHC molecules fitted in with the realization that the recognition of antigen by the T cell receptor is MHC‐restricted.Lanzavecchia's observations, together with these advances, led him to propose a new model of the B cell/helper T cell interaction. He employed a hapten (h) carrier system and studied the requirements to induce anti‐hapten antibody. He postulated that an anti‐h B cell endocytoses an h‐Q conjugate, where Q represents the carrier protein; the conjugate is then processed inside the B cell, and the peptide q/MHC complexes are subsequently presented by the B cell. The T helper cell specific for the nominal antigen Q can then recognize these peptide q/MHC complexes and deliver signal 2. The critical point is that the anti‐h B cell would not present peptide q/MHC complexes in the presence of Q and hR, but rather it would present peptide r/MHC complexes. The hapten must be coupled to Q if the anti‐h B cell is to endocytose Q via its antigen‐specific receptors.6.5Somewhat similarly, our extensive studies on the requirements to activate CD4 T cells show that the activation of Q‐specific responder T helper cells can be facilitated by R‐specific T helper cells in the presence of the conjugate Q‐R, but not when Q and R are present as separate molecules.Cohn disagreed with this solution for how linked recognition between cooperating CD4 T cells might be realized, and has discussed other possibilities,6.6We had proposed that effector helper T cells are required for antigen to optimally activate responder T helper cells, thus generating more effector helper T cells. How then are the first effector helper T cells generated? We refer to this as the priming problem.I proposed in a 1972 invited article,in the absence of antigen, slowly differentiate into effector helper T cells. Cohn envisioned that this pathway would not happen for CD4 T cells specific for self‐antigens, as self‐antigens are always present and their corresponding precursor helper T cells would be quickly inactivated by the mechanisms incorporated in our two‐signal model.Cohn stated7I would like to thank the four reviewers for their careful and considered comments. There is no doubt that these comments allowed me to improve the manuscript. Two comments made by different reviewers call for a response.One comment by one reviewer was: “What about B cell deficiency, where the (human) patient altogether lacks B cells? If B cells are important for providing the second antigen specific signal, how can these patients have a relatively adequate defense against viruses? Some practical examples based on current immunological thinking …would have made this account much more relevant and interesting.” My initial response to this comment, before discussing the science, is to say that the primary purpose of the article was to give my perspective on the history of a model that has remained pertinent for almost 50 years.Now for the science, I personally never expect all observations to fit into one picture, as no picture is complete, and because some of the frameworks we employ in “obtaining” our observations are likely wrong. The evidence for a mandatory role of B cells in generating murine immune responses appears to be highly contradictory. What to do about this situation was indeed the subject of recent discussions between Cohn and myself.Another reviewer felt the omission of discussing the role of co‐inhibitory signals, in the negative control of lymphocyte activation, was unbalanced. My perspective is that whether the one lymphocyte/multiple lymphocyte model holds is a fundamental question. This minimal model can accommodate a requirement or a lack of requirement for a co‐inhibitory signal in lymphocyte inactivation. What is particularly appealing to me is the clear role of co‐inhibitory signals in feedback regulation. I believe Sinclair originally proposed that antigen/IgG complexes could mediate an Fc‐mediated, negative signal to B cells, requiring the antigen to also bind to the B cell's antigen‐specific receptors.One reviewer pointed out that Cohn had a paper coming out in this journal, entitled ‘The real “danger” lies in failure to confront fundamentals',8Mel Cohn died in October 2018. A first version of this article was submitted before Cohn's death. His death occurred during the reviewing process. I asked the Associate Editor, who was dealing with this manuscript, if he could delay its consideration for a few months.I wish to give a brief explanation of the way I have responded to what I perceive as inaccuracies in the history Cohn gave of his and my involvement in the formulation of The Two Signal Model of Lymphocyte Activation. I have described above why Cohn's 1994 Wisdom of Hindsight articleFor years, I did not criticize in print Cohn's revisions or his further development of our ideas. I thought it more worthwhile that my students and I concentrate on experimentally testing predictions of the model we favoured,My 2014 article,


Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression.We conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status.We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation.Overall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation. Prostate cancer (PC) is a major public health issue in industrialized countries, mainly because patients relapse by castration-resistant (CR) disease after androgen deprivation , 2. PC i-omics approaches, able to monitor cancer-induced changes at the cellular level, are among the most promising strategies. Proteomic strategies, by measuring the abundance and activity of proteins, have the ability to directly reflect the functional activity of cells, and to point to deregulations in the most druggable cellular components. In this context, several proteomic studies started to map the landscape of the PC proteome , but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.b) Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.  http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: There are several studies in the literature about prostate cancer proteomics. However, in some cases these previous publications yielded data with limited reproducibility. It is sometimes difficult to distinguish between data which have implications in the clinic and others. Although data quality is accptable, there are some questions about the conclusions.Specific points:1. the reason for comparison of sensitive and resistant cell lines are not clear. This could imply that androgen receptor expression decreases during tumor progression what is not the case. It is difficult to draw any conclusion from this comparison.2. the descriütion of human cell lines in results is not necessary. They are very well known in scientific community.3. In the Results section, there are too many repetitions of that what has alrready been presented in Materials and Methods.4. it is not clear whether this manuscript could confirm previous findings in the proteomic field.Reviewer #2: M Katsogiannou et al presented a study that aimed to investigate and explore the proteome and phosho-proteome of four well established prostate cancer cell lines. Utilizing a SILAC-based Mass Spectrometry approach, the data show set of proteins that are commonly and highly expressed in all four cell lines, as well as differentially expressed proteins between castrate-resistant and castrate-sensitive cells. Other comparisons such as proteins up-regulated in cancer cell lines compared to non-tumorigenic cell is demonstrated. Phospho-proteomic data is also presented for the cell lines including presentation of the proteome and phosphor-proteome in a molecular network to identify candidate biomarkers for Prostate cancer. Overall, the study is well-done and provides a starting point to further explore the role of potential candidate proteins identified through this study in prostate cancer. Although the study adds some new potential proteins to the list as target molecules and biomarkers, it reconfirms a lot of information that is already published in the field.1)There are other studies that have used proteomic -based approaches on prostate cancer cells or conditioned media from prostate cancer cells? What is the novelty of the approach used in this study?2) Please show if the data can be used for miRNA target prediction? It would be a good addition to the study as miRNA’s have great potential as biomarkers in cancer.3) Add PCA plot to show that each cell line has a unique protein and phosphor-protein signature and add Venn-diagrams to show common and unique expressors for a) cancer cell lines compared to non-tumorigenic cells b) castrate resistant cells compared to castrate sensitive cells.4) Are there common expressors and unique expressors between DU145 and PC3. This could perhaps help identify candidate biomarkers and targets for highly metastatic and aggressive disease.5) Show box-plots for expression levels of TAGLN2 and HNRNPN1 in an additional figure. Validation of differences in protein levels must be shown.5) The resolution is images in the figures needs to be increased. Figures are hard to read as of now.Reviewer #3: The manuscript from Katsogiannou et al. showed a large SILAC-based Mass Spectrometry experiment that allowed to map the proteomes and phosphoproteomes of PNT1A, LNCaP, DU145 and PC3 prostate cancer cell lines, and reveal different signaling networks associated with the cellular context of each cell line, possibly reflecting the pathological features of human Prostate Cancer .The experimental data are strong, rigorous and well presented, in particular the deep comparison of the four cell lines for the identification of the housekeeping proteome vs the most significant variations across the samples.However, some critical points emerged, and should be clarified/investigated more in depth:1. The androgen-dependent/castration-sensitive cell line LNCaP has been compared with the androgen-independent/castration-resistant cells DU145 and PC3. However, the best in vitro models for comparing these two PrCa conditions would be LNCaP vs C4-2. Why the authors did exclude C4-2 cells from their high-throughput analysis?2. The ANOVA analysis of the proteomics/phosphoproteomics data highlighted several proteins/phosphosites that vary significantly in various comparisons . For some of these variations, the authors even claimed that “these proteins could constitute markers of oncogenic transformation”. To support this kind of statements, the authors should provide “wet-lab” validations of their high-throughput results, at least on representative targets among those described in the text . This type of validation would also help the authors focusing on the most important pathways, rather than leaving the reader with a comprehensive description of all signaling networks potentially involved in the regulation of PrCa malignant progression.Minor points:1. Page 8, Lanes 303-305): DU145 are derived from CNS metastasis, and PC3 from bone metastasis. Please revise the sentence.2. Figure 2: it should be useful to include a “title/legend” to each bar graph (e.g. LNCaP vs PNT1A in the panel A), showing the comparisons as described in the figure legend text.3. Figure 3: similar to the previous point, it should be useful to include a “visible” title/legend to each panel .**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article Department of Medical, Oral and Biotechnological Sciences"G. d'Annunzio" University of Chieti-Pescara https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  19 Sep 2019We copied and pasted here the contents of the uploaded file: "Response to reviewers". We recommend the editor and the reviewers to check that file for a more clear and detailed information. In addition, some figures have been added to the mentioned file to address the questions raised by the reviewers. Journal requirements:When submitting your revision, we need you to address these additional requirements.1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttp://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfWe have now revised our manuscript and file naming aiming at meeting the PLOS ONE's style requirements. We put special emphasis in the correction of author’s affiliations. We however must follow the requirements from our institutions which have very strict policies concerning this point. We hope to find a solution on this point. 2. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository  and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.Ok. we decided to remove it as this was not a core part of the study.3. Thank you for stating the following in the Financial Disclosure section:"This work was supported by the French "Plan Cancer 2009-2013" . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." We note that one or more of the authors are employed by a commercial company: 'ProGeLife, Marseille, France'.a) Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.Please also include the following statement within your amended Funding Statement.“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.b) Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc. http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript” to the funding statement and to the cover letter.We added the sentence: “The funder ProGeLife provided support in the form of salaries for author AV, but had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of the authors are articulated in the ‘author contributions’ section” to the funding statement and to the cover letter.We added the sentence “This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.” to the funding statement and to the cover letter. Please do not hesitate to contact us if we did not understand something correctly.Reviewer #1: There are several studies in the literature about prostate cancer proteomics. However, in some cases these previous publications yielded data with limited reproducibility. It is sometimes difficult to distinguish between data which have implications in the clinic and others. Although data quality is acceptable, there are some questions about the conclusions.We agree that reproducibility is a major issue in cancer research, and that the translation from observations to clinics is often uncertain. Here are few choices we made to try to mitigate these issues:We avoided the imputation of missing values in our proteomics datasets, and performed stringent filtering to reduce the impact of technical noise;- We tried to be particularly cautious in the wording of the manuscript, and to not overstate the clinical implications of the candidates obtained during the exploration of our datasets;- We selected cell lines widely used as models in prostate cancer research;- We carefully checked the literature to evaluate if we retrieve (and thereby reproduce) proteins already described associated with prostate cancer.1. the reason for comparison of sensitive and resistant cell lines are not clear. This could imply that androgen receptor expression decreases during tumor progression what is not the case. It is difficult to draw any conclusion from this comparison.We are not sure to understand this comment. Indeed, the two castration-resistant cell lines do lack the androgen receptor, and the castration-sensitive cell line expresses it . The goal of the comparison of sensitive versus resistant cells was thereby to assess pathways that could be differentially activated in these cell lines, and thereby point towards mechanisms implemented by the resistant cell lines to overcome, among others, the absence of the androgen receptor. Several mechanisms are known to be involved in the emergence of the malignant progression of prostate cancer and resistance phenotype, through AR-related and non-AR-related pathways. For example, post-transcriptional modification using miRNA, epigenetic alterations, alternative splicing or gene fusion are parts of the hallmark of CRPC . We identified several proteins involved in these mechanisms in our proteomics screening. In 30% of CRPC, the AR promoter region has been described to be hypermethylated, resulting in the loss of AR expression in those tumors .In the manuscript, we clarified the introduction to state precisely that resistance is associated with androgen receptor pathway changes (line 8 in the introduction). “This progression involves several molecular mechanisms such as ligand-independent androgen receptor activation, androgen receptor (AR) loss or adaptive upregulation of anti-apoptotic genes (for review 3)”.Pienta KJ, Abate-Shen C, Agus DB, Attar RM, Chung LW, Greenberg NM, Hahn WC, Isaacs JT, Navone NM, Peehl DM, Simons JW, Solit DB, Soule HR, VanDyke TA, Weber MJ, Wu L, Vessella RL. The current state of preclinical prostate cancer animal models. Prostate. 2008May 1;68(6):629-39. doi: 10.1002/pros.20726CunninghamD, You Z. In vitro and in vivo model systems used in prostate cancer research. J Biol Methods. 2015;2(1). pii: e17.2. the descriütion of human cell lines in results is not necessary. They are very well known in scientific community.These prostate cell lines are indeed well-known in the prostate cancer research community. We however expect the dataset we are producing to be used as a resource by other researchers with diverse expertise, such as bioinformatics, and think it’s important to state their biological features in details. We however removed the description of the cell lines from the Material and Methods sections to reduce redundancies.3. In the Results section, there are too many repetitions of that what has already been presented in Materials and Methods.We have reviewed the Results and Material & Methods sections in details and removed redundancies. 4. it is not clear whether this manuscript could confirm previous findings in the proteomic field.Apart from the proteins for which we manually curated the literature to validate their previous implication in prostate cancer, and that are described in the text, we also considered comparing our results with existing “omics” large-scale datasets. We identified prostate cancer publications focusing on tissue proteomics  and on cell line & tissue phosphoproteomics . We also found an update of Iglesias-Gato et al. 2016 on the proteome of prostate cancer bone metastasis . Finally, genomics data are also available for ~60 cancer cell lines in the NCI60 resource. We discuss below the comparison of our dataset with each of these resources. Please note that this answer is similar to the one proposed for Reviewer 2 first comment.Iglesias-Gato, D. et al. The Proteome of Primary Prostate Cancer. European Urology 69, 942–952 (2016).Drake, J. M. et al. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166, 1041–1054 (2016).Iglesias-Gato, D. et al. The Proteome of Prostate Cancer Bone Metastasis Reveals Heterogeneity with Prognostic Implications. Clin. Cancer Res. 24, 5433–5444 (2018).- Comparison with NCI60 genomics data resourcehttps://dtp.cancer.gov/discovery_development/nci-60/), which provide genomics data for a large panel of reference cell lines. Our goal was to try to link genomics mutations in cell lines with proteomics level deregulations. However, only 2 of the 4 cell lines that we profiled in our experiment are described in this database (DU145 and PC3), and we decided not to pursue in this direction.We checked the NCI-60 tumor cell line screen In Drake et al., the authors integrate transcriptomics and phosphoproteomics (but no proteomics) data for many tumor tissues and 3 prostate cancer cell lines. We studied their paper in-depth, in particular the Figure 1, which shows that the cell lines data were clustering apart from the tissue data. We contacted the authors when the paper was published, and they gave us a more detailed presentation of their Figure 1. Unfortunately, we have only the DU145 cell line in common, and we were thereby not able to compare our clusterings. In addition, a direct comparison of our phosphoproteomics raw data is not pertinent as, contrarily to us, their protocole implies vanadate (a protein-phosphotyrosine phosphatase inhibitor) treatment, in order to increase the detection of the phospho-tyrosine.- Iglesia-Gato et al. 2016We also compared the proteomics data we have generated with the ones produced in Iglesias-Gato 2016. We selected the normalised ratios  of the 9829 identified proteins in the Iglesias-Gato dataset. In our dataset, we took the normalised ratios of the 3219 identified proteins. The heatmap below (check Response to reviewers file) was created based on the Pearson correlation values between the expression levels of 2673 proteins identified in both experiments. We can observe that cell lines clusterize apart from tissue data. This clustering is similar to the one obtained by in prostate cancer transcriptomics and phosphoproteomics  as well as for other cancer types and omics data . It has also been observed for breast cancer that among different types of omics, proteins levels had the lowest correlation between tumors and cell lines . Overall, these observations highlight the large dissimilarities between tissue samples and cell lines, emphasizing the need to obtain molecular profiles from both types of resources.Domcke, S., Sinha, R., Levine, D. A., Sander, C. & Schultz, N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications 4, 2126 (2013).Drake, J. M. et al. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166, 1041–1054 (2016).Jiang G, Zhang S, Yazdanparast A, et al. Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer. BMC Genomics. 2016;17 Suppl 7(Suppl 7):525.We also conducted a meta-analysis using the R package “MetaDE”. Our goal was to compare the protein level trends among tissue and cell lines. We used a Random Effects Model (REM) approach for the meta-analysis, since it allows heterogeneity in the effect sizes between the different datasets (Tissue VS cell lines). We took PNT1A as the reference for our SILAC experiment. Proteins were selected as those displaying an FDR corrected p-value < 0.05. The results are the following:- LNCaP: 114 proteins have a similar behaviour in cell lines as compared to tumor tissues. This means that the expression level changes of these proteins between LNCaP and PNT1A are similar to those between control tissue and tumorigenic tissue.- DU145: 81 proteins have a similar behaviour.- PC3: 73 proteins have a similar behaviour.These results indicate that LNCaP is the cell line with protein levels displaying the most similar behavior as compared to the tumor tissue. Iglesia-gato 2018We finally applied our previously mentioned comparison protocol to the data produced by Iglesias-Gato in 2018. In this case, they identify 9828 proteins, 2673 of them also match with our identified proteins. Here, we also observe that our protein expression data clusterize separately from the one resulting from tissue samples (See figures in the Response to reviewers file).Overall, the main strengths of our work are to profile four widely used prostate cell lines and to do this both at the proteomics and phosphoproteomics levels. In addition, as shown in the previous comparisons, our datasets are complementary to the recent literature on prostate cancer transcriptomics, proteomics or phosphoproteomics, and could be useful for the community working with these cell lines in routine. Furthermore, we also propose a bioinformatics exploration of our datasets, as well as their integration into a molecular network, allowing the identification of processes that are not detectable with a traditional analyses of differentially expressed proteins/sites.Reviewer #2: M Katsogiannou et al presented a study that aimed to investigate and explore the proteome and phosho-proteome of four well established prostate cancer cell lines. Utilizing a SILAC-based Mass Spectrometry approach, the data show set of proteins that are commonly and highly expressed in all four cell lines, as well as differentially expressed proteins between castrate-resistant and castrate-sensitive cells. Other comparisons such as proteins up-regulated in cancer cell lines compared to non-tumorigenic cell is demonstrated. Phospho-proteomic data is also presented for the cell lines including presentation of the proteome and phosphor-proteome in a molecular network to identify candidate biomarkers for Prostate cancer. Overall, the study is well-done and provides a starting point to further explore the role of potential candidate proteins identified through this study in prostate cancer. Although the study adds some new potential proteins to the list as target molecules and biomarkers, it reconfirms a lot of information that is already published in the field.We thank the reviewer for this accurate and detailed summary of our manuscript. Many thanks. Indeed, one of the manuscript's main goals is to provide a resource as a starting point for further exploration of proteins related to PC progression. 1)There are other studies that have used proteomic -based approaches on prostate cancer cells or conditioned media from prostate cancer cells? What is the novelty of the approach used in this study?Apart from the proteins for which we manually curated the literature to validate their previous implication in prostate cancer, and that are described in the text, we also considered comparing our results with existing “omics” large-scale datasets. Importantly, we did not identify other proteomics/phosphoproteomics approaches focusing on prostate cell lines, but we identified prostate cancer publications focusing on tissue proteomics  and on cell line & tissue phosphoproteomics . We also found an update of Iglesias-Gato et al. 2016 on the proteome of prostate cancer bone metastasis . Finally, genomics data are also available for ~60 cancer cell lines in the NCI60 resource. We discuss below the comparison of our dataset with each of these resources. Please note that this answer is similar to the one proposed for Reviewer 1 fourth comment.Iglesias-Gato, D. et al. The Proteome of Primary Prostate Cancer. European Urology 69, 942–952 (2016).Drake, J. M. et al. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166, 1041–1054 (2016).Iglesias-Gato, D. et al. The Proteome of Prostate Cancer Bone Metastasis Reveals Heterogeneity with Prognostic Implications. Clin. Cancer Res. 24, 5433–5444 (2018).- Comparison with NCI60 genomics data resourcehttps://dtp.cancer.gov/discovery_development/nci-60/), which provide genomics data for a large panel of reference cell lines. Our goal was to try linking genomics mutations in cell lines with proteomics level deregulations. However, only 2 of the 4 cell lines that we profiled in our experiment are described in this database (DU145 and PC3), and we decided not to pursue in this direction.We checked the NCI-60 tumor cell line screen In Drake et al., the authors integrate transcriptomics and phosphoproteomics (but no proteomics) data for many tumor tissues and 3 prostate cancer cell lines. We studied their paper in-depth, in particular the Figure 1, which shows that the cell lines data were clustering apart from the tissue data. We contacted the authors when the paper was published, and they gave us a more detailed presentation of their Figure 1. Unfortunately, we have only the DU145 cell line in common, and we were thereby not able to compare our clusterings. In addition, a direct comparison of our phosphoproteomics raw data is not pertinent as, contrarily to us, their protocole implies vanadate (a protein-phosphotyrosine phosphatase inhibitor) treatment, in order to increase the detection of the phospho-tyrosine.- Iglesia-Gato et al. 2016We also compared the proteomics data we have generated with the ones produced in Iglesias-Gato 2016. We selected the normalised ratios  of the 9829 identified proteins in the Iglesias-Gato dataset. In our dataset, we took the normalised ratios of the 3219 identified proteins. The heatmap below (check response to reviewers file) )was created based on the pearson correlation values between the expression levels of 2673 proteins identified in both experiments. We can observe that cell lines clusterize apart from tissue data. This clustering is similar to the one obtained by in prostate cancer transcriptomics and phosphoproteomics  as well as for other cancer types and omics data . It has also been observed for breast cancer that among different types of omics, proteins levels had the lowest correlation between tumors and cell lines . Overall, these observations highlight the large dissimilarities between tissue samples and cell lines, emphasizing the need to obtain molecular profiles from both types of resources.Domcke, S., Sinha, R., Levine, D. A., Sander, C. & Schultz, N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications 4, 2126 (2013).Drake, J. M. et al. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166, 1041–1054 (2016).Jiang G, Zhang S, Yazdanparast A, et al. Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer. BMC Genomics. 2016;17 Suppl 7(Suppl 7):525.We also conducted a meta-analysis using the R package “MetaDE”. Our goal was to compare the protein level trends among tissue and cell lines. We used a Random Effects Model (REM) approach for the meta-analysis, since it allows heterogeneity in the effect sizes between the different datasets (Tissue VS cell lines). We took PNT1A as the reference for our SILAC experiment. Proteins were selected as those displaying an FDR corrected p-value < 0.05. The results are the following:- LNCaP: 114 proteins have a similar behaviour in cell lines as compared to tumor tissues. This means that the expression level changes of these proteins between LNCaP and PNT1A are similar to those between control tissue and tumorigenic tissue.- DU145: 81 proteins have a similar behaviour.- PC3: 73 proteins have a similar behaviour.These results indicate that LNCaP is the cell line with protein levels displaying the most similar behavior as compared to the tumor tissue. Iglesia-gato 2018We finally applied our previously mentioned comparison protocol to the data produced by Iglesias-Gato in 2018. In this case, they identify 9828 proteins, 2673 of them also match with our identified proteins. Here, we also observe that our protein expression data clusterize separately from the one resulting from tissue samples (check figures in Response to reviewers file).Overall, the main strengths and novelty of our work are to profile four widely used prostate cell lines and to do this both at the proteomics and phosphoproteomics levels. As shown in the previous comparisons, our datasets are complementary to the recent literature on prostate cancer transcriptomics, proteomics or phosphoproteomics, and could be useful for the community working with these cell lines in routine. Furthermore, we also propose a bioinformatics exploration of our datasets, as well as their integration into a molecular network, allowing the identification of processes that are not detectable with a traditional analyses of differentially expressed proteins/sites.2) Please show if the data can be used for miRNA target prediction? It would be a good addition to the study as miRNA’s have great potential as biomarkers in cancer.This is indeed a very interesting point. During our review of the literature, we explored some interesting cases of miRNA-target interactions that could be relevant in the context of our prostate proteomics study . For instance, the regulation of VDAC1 by miR-29 is very appealing. VDAC1 is a mitochondrial protein directly involved in apoptosis (it promotes the apoptosis of tumor cells), and it harbours a miR-29a target site. The overexpression of miR-29a has been shown to result in downregulation of VDAC1 . The protein level profile of VDAC1 in the different prostate cells lines is shown below (check Response to reviewers file). Low levels in the expression of VDAC1 could lead to a decrease in the apoptosis of tumor cells resulting in more aggressive cancers as represented by PC3 and DU145 lines. miR-29 might be involved in this process, it would be interesting to profile its expression in these cell lines. We however acknowledge that we did not design our study to consider miRNA-target predictions. For instance, the network we are building considers proteins and phosphosites measured in our study, but we did not measured miRNA. It would be an attractive follow-up.Vanacore D et al. Micrornas in prostate cancer: an overview. Oncotarget. 2017 Jul 25;8(30):50240-50251.Bargaje R et al. Identification of novel targets for miR-29a using miRNA proteomics. PLoS One. 2012;7(8):e43243. doi:10.1371/journal.pone.00432433) Add PCA plot to show that each cell line has a unique protein and phosphor-protein signature and add Venn-diagrams to show common and unique expressors for a) cancer cell lines compared to non-tumorigenic cells b) castrate resistant cells compared to castrate sensitive cells.Please, check the plots and comments in the Response to Reviewers file.4) Are there common expressors and unique expressors between DU145 and PC3. This could perhaps help identify candidate biomarkers and targets for highly metastatic and aggressive disease.This is a very interesting comparison. We performed t-test comparisons between the two castration-resistant cell lines to determine differentially expressed proteins and phosphosites. We finally did not presented this information because we first aimed at exploring the molecular events driving the global progression of prostate cancer to resistance, i.e., we focused on the mechanisms that could be common in the two resistant cell lines as compared to the sensitive one. However, global similarities and differences between PC3 and DU145 can be appraised in the context of their comparison to PNT1A and LNCaP in the manuscript. 5) Show box-plots for expression levels of TAGLN2 and HNRNPN1 in an additional figure. Validation of differences in protein levels must be shown.We assumed that the reviewer refers to TAGLN2 and HNRNPA1. These two proteins are interesting candidates because they have been associated with phosphosites significantly different from one cell line to another according to an ANOVA test. We are not sure to understand the comment on the validation of differences in protein levels. Both proteins are housekeeping . All proteins discussed in the manuscript have been identified in the proteomics experiment with an FDR < 1%. TAGLN2 is in addition under-expressed in LNCaP cell line (FDR=0.007), and over-expressed in DU145 (FDR=0.02) and PC3 (FDR=0.049) cell lines, as compared with PNT1A, and over-expressed in resistant compared to sensitive cell lines (FDR=0.01). As suggested by the reviewer, box-plots describing the protein and phosphosite levels of TAGLN2 and HNRNPA1 have been generated and are included in the submission of the new version of the manuscript as supplementary figures 4 and 5. 5) The resolution is images in the figures needs to be increased. Figures are hard to read as of now.We increased the resolution the final version of the manuscript. In addition, we increased the size and quality of the labels in figures 2 and 3. Reviewer #3: The manuscript from Katsogiannou et al. showed a large SILAC-based Mass Spectrometry experiment that allowed to map the proteomes and phosphoproteomes of PNT1A, LNCaP, DU145 and PC3 prostate cancer cell lines, and reveal different signaling networks associated with the cellular context of each cell line, possibly reflecting the pathological features of human Prostate Cancer .The experimental data are strong, rigorous and well presented, in particular the deep comparison of the four cell lines for the identification of the housekeeping proteome vs the most significant variations across the samples.We thank the reviewer for his encouraging comments and for the critical points discussed, which help us to improve the quality of our manuscript. However, some critical points emerged, and should be clarified/investigated more in depth:1. The androgen-dependent/castration-sensitive cell line LNCaP has been compared with the androgen-independent/castration-resistant cells DU145 and PC3. However, the best in vitro models for comparing these two PrCa conditions would be LNCaP vs C4-2. Why the authors did exclude C4-2 cells from their high-throughput analysis?Several other PCa models exist, such as C4-2, a particularly interesting LNCaP-derived cell line that constitutes a compelling androgen-independent model. We however had to make a choice in the number of cell lines during the initial design of the study, because of the amount of experimental material and subsequent data produced. We decided not to work with derived cell lines (from LNCaP or other cells), because of the genetic proximity. We wanted instead to identify mechanisms of resistance that could have emerged in genetically different cell lines, derived from separate metastasis. The cells lines described in our study are four of the most widely used cell lines in prostate cancer research  that we routinely use in the lab. Pienta KJ, Abate-Shen C, Agus DB, Attar RM, Chung LW, Greenberg NM, Hahn WC, Isaacs JT, Navone NM, Peehl DM, Simons JW, Solit DB, Soule HR, VanDyke TA, Weber MJ, Wu L, Vessella RL. The current state of preclinical prostate cancer animal models. Prostate. 2008May 1;68(6):629-39. doi: 10.1002/pros.20726CunninghamD, You Z. In vitro and in vivo model systems used in prostate cancer research. J Biol Methods. 2015;2(1). pii: e17.2. The ANOVA analysis of the proteomics/phosphoproteomics data highlighted several proteins/phosphosites that vary significantly in various comparisons . For some of these variations, the authors even claimed that “these proteins could constitute markers of oncogenic transformation”. To support this kind of statements, the authors should provide “wet-lab” validations of their high-throughput results, at least on representative targets among those described in the text . This type of validation would also help the authors focusing on the most important pathways, rather than leaving the reader with a comprehensive description of all signaling networks potentially involved in the regulation of PrCa malignant progression.www.proteomexchange.org) via the PRIDE partner repository with datasets identifiers PXD004970 and PXD004992.]).The generation of the SILAC proteomics and phosphoproteomics dataset from the 4 cell lines and with 3 replicates was a large and complex experimental work, and we decided to focus our subsequent analyses on the in-depth data exploration of this dataset rather than on functional experimental validation of cherry-picked candidates. In the design of our study, we took inspiration from references in the proteomics field, in particular from Matthias Mann lab papers on cell line proteomics characterizations , but also from a reference in prostate cancer analysis in which the authors produced phosphoproteomics and genomics for prostate cancer, both on cell lines and real tumor tissue . Similarly to these studies, we concentrated our efforts on the data generation and their exploration, with the objective to produce a resource that could subsequently be used as a starting point for further hypothesis and experimental investigations .Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins. Mol Cell Proteomics 11, (2012).Drake, J. M. et al. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166, 1041–1054 (2016).Minor points:1. Page 8, Lanes 303-305): DU145 are derived from CNS metastasis, and PC3 from bone metastasis. Please revise the sentence.Thanks for catching this mistake! This has been corrected.2. Figure 2: it should be useful to include a “title/legend” to each bar graph (e.g. LNCaP vs PNT1A in the panel A), showing the comparisons as described in the figure legend text.Done3. Figure 3: similar to the previous point, it should be useful to include a “visible” title/legend to each panel .Done. We have now included a title for each subfigure to further clarify the different plots as suggested. Moreover, we increased the size of the label titles and numbers in the axis. DoneAttachmentResponse To Reviewers.pdfSubmitted filename: Click here for additional data file. 8 Oct 2019Integrative proteomic and phosphoproteomic profiling of prostate cell linesPONE-D-19-20799R1Dear Dr. Valdeolivas,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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Explanations provided in response to reviewer comments are decent as authors provide a convincing response regarding the reasons behind the questions they asked in the study. Overall, the manuscript is well- done and provides a good starting point to further explore the potential candidate proteins and conduct follow-up studies such as miRNA targets etc which the authors of the study themselves appreciate as a strength of the study.Reviewer #3: (No Response)**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article (If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? 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