However, knowledge regarding the effects of FGF-23 on cardiac fibrogenesis remains limited. This study investigated whether FGF-23 modulates cardiac fibroblast activity and explored its underlying mechanisms. We performed MTS analysis, 5-ethynyl-2′-deoxyuridine assay, and wound-healing assay in cultured human atrial fibroblasts without and with FGF-23  to analyze cell proliferation and migration. We found that FGF-23  increased proliferative and migratory abilities of human atrial fibroblasts. Compared to control cells, FGF-23 (25 ng/mL)-treated fibroblasts had a significantly higher Ca2+ entry and intracellular inositol 1,4,5-trisphosphate (IP3) level (assessed by fura-2 ratiometric Ca2+ imaging and enzyme-linked immunosorbent assay). Western blot analysis showed that FGF-23 (25 ng/mL)-treated cardiac fibroblasts had higher expression levels of calcium release-activated calcium channel protein 1 (Orai1) and transient receptor potential canonical (TRPC) 1 channel, but similar expression levels of α-smooth muscle actin, collagen type IA1, collagen type Ⅲ, stromal interaction molecule 1, TRPC 3, TRPC6 and phosphorylated-calcium/calmodulin-dependent protein kinase II when compared with control fibroblasts. In the presence of ethylene glycol tetra-acetic acid  or U73122 , control and FGF-23-treated fibroblasts exhibited similar proliferative and migratory abilities. Moreover, polymerase chain reaction analysis revealed that atrial fibroblasts abundantly expressed FGF receptor 1 but lacked expressions of FGF receptors 2-4. FGF-23 significantly increased the phosphorylation of FGF receptor 1. Treatment with PD166866  attenuated the effects of FGF-23 on cardiac fibroblast activity. In conclusion, FGF-23 may activate FGF receptor 1 and subsequently phospholipase C/IP3 signaling pathway, leading to an upregulation of Orai1 and/or TRPC1-mediated Ca2+ entry and thus enhancing human atrial fibroblast activity.Fibroblast growth factor (FGF)-23 induces hypertrophy and calcium (Ca Cardiac fibrosis is a common pathological change in the late stage of most heart disease types and represents a major threat to human health. Substantial evidence indicates that cardiac fibroblasts play a crucial role in the pathogenesis of cardiac fibrosis ,2. In re2+) homeostasis. Moreover, multiple studies have indicated the crucial involvement of FGF-23 in heart failure pathophysiology. A high circulating FGF-23 level was reported to be associated with high risks of cardiovascular diseases, heart failure, and mortality in the elderly population [2+-ATPase, a major regulatory protein that extrudes cytosolic Ca2+ to the sarcoplasmic reticulum, thereby enhancing the sarcoplasmic reticulum Ca2+ content in cardiomyocytes and leading to arrhythmogenesis [Fibroblast growth factor (FGF)-23, a member of the FGF family, circulates as an endocrine regulator for phosphate and calcium  on human atrial fibroblasts and found that FGF-23 -treated cardiac fibroblasts had a significantly higher migratory ability than did control cells A. MTS asThe expression profile of four distinct FGF receptors (1–4) in cardiac fibroblasts was determined through quantitative polymerase chain reaction (PCR). The results revealed that human atrial fibroblasts abundantly expressed FGF receptor 1 but lacked the expressions of FGF receptors 2–4 A. FGF-232+ levels in the presence of extracellular Ca2+ at 1.8 mM -treated human atrial fibroblasts than did control cells -treated cardiac fibroblasts were found to have a similar intracellular Ca2+ content at baseline. Following treatment with thapsigargin, an endoplasmic reticulum (ER) Ca2+-ATPase inhibitor, control and FGF-23 (25 ng/mL for 24 h)-treated cardiac fibroblasts had a similar ER Ca2+ release -treated cardiac fibroblasts had both significantly higher ER Ca2+ release and Ca2+ entry than did control cells. Moreover, FGF-23 (25 ng/mL for 24 h)-treated cardiac fibroblasts had a significantly higher steady-state of intracellular Ca2+ level than control cells . However, FGF-23 (25 ng/mL for 48 h)-treated cardiac fibroblasts had an insignificant higher steady-state of intracellular Ca2+ level than control cells . These findings suggest that FGF-23 augments Ca2+ entry, leading to the elevation of intracellular Ca2+ level, which stimulates downstream signaling and consequent fibroblast activation. Additionally, FGF-23 may enhance Ca2+ reuptake into ER to normalize the intracellular Ca2+ levels. We also found that ethylene glycol tetra-acetic acid (EGTA), a free Ca2+ chelator, significantly reduced the stimulatory effects of FGF-23 on cardiac fibroblast activity. Control cells, cardiac fibroblasts treated with EGTA (1 mM) and FGF-23 (25 ng/mL) combined with EGTA (1 mM)-treated cardiac fibroblasts had a similar OD value measured by MTS assay (Treatment with FGF-23 (25 ng/mL) rapidly elevated intracellular Cat 1.8 mM A, but diroblasts B. Moreovol cells B. In a C release A. FGF-23TS assay B. LikewiTS assay B.3) production, we measured FGF-23-induced changes in intracellular Ca2+ levels in the absence and presence of U73122 (a PLC inhibitor) and 2-aminoethoxydiphenyl borate . The rapid increase in intracellular Ca2+ levels induced by FGF-23 was abolished by pretreatment of U73122 (1 μM) and 2-APB (50 uM), respectively (3) levels in cardiac fibroblasts. However, control and FGF-23 (25 ng/mL)-treated cardiac fibroblasts had similar phosphorylation levels of calcium/calmodulin-dependent protein kinase II (CaMKⅡ), suggesting that FGF-23 did not activate CaMKⅡ signaling in cardiac fibroblasts -treated cardiac fibroblasts had higher protein expression levels of calcium release-activated calcium channel protein 1 (Orai1), but had similar protein expression levels of stromal interaction molecule 1 (STIM1) when compared with control cells -treated and FGF-23 (25 ng/mL) combined with U73122 (1 μM)-treated cardiac fibroblasts. Likewise, control, U73122 (1 μM)-treated fibroblasts, and FGF-23 (25 ng/mL) combined with U73122 (1 μM)-treated fibroblasts had similar Orai1 expressions (Treatment with U73122 (1 μM) significantly mitigated the highly elevated ER Caroblasts A. Ca2+ eressions B. In addressions B. We alsressions B. Overal3 signaling pathway, leading to an upregulation of Orai1-mediated Ca2+ entry and thus enhancing cardiac fibroblast activity. Accordingly, FGF-23 may be a potential target for regulating cardiac fibrosis, especially in patients with chronic kidney disease who generally have higher circulating FGF-23 levels.Although FGF-23 has been shown to stimulate the proliferation and migration of neonatal rat cardiac fibroblasts ,11, mechA previous study has shown that the mean plasma concentrations of FGF-23 were 20 ± 5 ng/mL in peritoneal dialysis patients . TreatmeLaboratory evidence indicated that FGF-23 directly induces left ventricular hypertrophy through the activation of the FGF receptor . The mam2+ level, which has been linked to a high risk of cardiac hypertrophy [2+ content and Ca2+ leak in cardiomyocytes [2+ is a critical regulator of multiple functions of cardiac fibroblasts [2+ level contributed to increased atrial fibroblast activity [3 production and increased Ca2+ entry in human atrial fibroblasts. In response to augmented Ca2+ entry, ER Ca2+-ATPase would be activated to operatively suppress the increasing cytosolic Ca2+ levels. We found that FGF-23 increased protein expression levels of ER Ca2+-ATPase in cardiac fibroblasts -treated cardiac fibroblasts also had considerably higher ER Ca2+ stores than FGF-23 (25 ng/mL for 24 h)-treated and control fibroblasts. However, an increased level of ER Ca2+-ATPase protein expression may not necessarily indicate enhanced reaction activity. Measurements of ER Ca2+-ATPase reaction rate could provide the direct evidence to support this conclusion. Moreover, the upregulated cell proliferative and migratory abilities of FGF-23-treated cardiac fibroblasts were eliminated by EGTA. Our results imply that FGF-23 modulated Ca2+ homeostasis to promote human atrial fibroblast activity.Exposure of primary mouse cardiomyocytes to FGF-23 led to an elevated intracellular Caertrophy . FGF-23 myocytes ,20. Moreroblasts . Our preactivity ,23,24. Iroblasts , which m2+/calcineurin/NFAT pathway, ultimately leading to cardiomyocyte hypertrophy [2+ entry pathway in cardiac fibroblasts is store-operated Ca2+ entry [3. IP3 induces Ca2+ release from ER via the IP3 receptor. The depletion of Ca2+ stores in ER leads to the activation of STIM1 which is the Ca2+ sensing protein distributed mainly on the surface of the ER. Activated STIM1 will then translocate to the plasma membrane where it can interact with Orai1, the plasma membrane Ca2+ channel, inducing store-operated Ca2+ entry [2+ homeostasis alterations [2+ response to FGF-23 in human atrial fibroblasts showed that intracellular Ca2+ level was not elevated in the absence of extracellular Ca2+, suggesting that FGF-23 induces a rapid increase in intracellular Ca2+ levels through stimulating Ca2+ entry. In addition, 2-APB is a chemical that blocks store-operated Ca2+ entry and IP3-induced Ca2+ release [3 inhibitor [3 inhibitor abolished the FGF-23-evoked increases in the intracellular Ca2+ levels, implying that FGF-23 actually results in IP3 production. Moreover, the FGF-23-induced upregulations of Orai1 expression and cardiac fibroblast activity were both suppressed by PLC inhibition. Accordingly, our findings revealed that FGF-23 enhances store-operated Ca2+ entry in cardiac fibroblasts through activating PLC/IP3/Orai1 signaling, leading to the stimulatory effects on human atrial fibroblast activity. However, the mechanism underlying the stimulatory effect of FGF-23 on PLC activation is not clear. This study did not investigate Orai1 expression after FGFR1 modulation. Thus, the regulatory mechanisms between FGF receptor 1 and PLC/Orai1 signaling remain to be elucidated.Our previous investigations have reported that FGF-23 increased arrhythmogenesis through the activation of CaMKII signaling in atrial myocytes and pulmonary vein cardiomyocytes ,20. In tertrophy . Moreoveertrophy ,26. Howe2+ entry ,27. PLC 2+ entry ,28. Vent2+ entry . Inhibiterations . In the  release . Howevernhibitor . Treatme2+ entry in cardiac fibroblasts and mediate cardiac fibrogenesis [3 via conformational coupling with IP3 receptor, or following IP3-mediated depletion of ER Ca2+ stores [2+ leakage from the ER in neonatal rat ventricular myocytes [2+ signaling in cardiac fibroblasts.TRPC channels are sarcolemmal cation channels and contain seven members (TRPC1-7). TRPC3 and TRPC6 have been demonstrated to regulate Caogenesis . Our stu+ stores ,33. Upre+ stores ,35. TRPCmyocytes . HoweverFGF-23 has been shown to promote myocardial fibrosis in mice with left coronary artery ligation-induced myocardial infarction, but not in sham-operated mice . FGF-23 2 at 37 °C. Cells used in this study were from passages 4–6 to avoid possible variations in cellular function. Cardiac fibroblasts treated without and with recombinant mouse FGF-23  in the presence and absence of an FGF receptor 1 antagonist , a free Ca2+ chelator , or a PLC inhibitor  for 48 h were harvested for further analysis.Human atrial fibroblasts were purchased from Lonza Research Laboratory . As described previously , cells wThe proliferation of cardiac fibroblasts was determined using a commercial MTS kit , as described previously . Cardiac® Plus EdU Flow Cytometry Assay Kits  according to the manufacturer’s instructions. Briefly, cardiac fibroblasts were treated with FGF-23  for 48 h and labelled with Click-iT® EdU (10 μM) for 24 h prior to the end of the treatments. After washing with 1% bovine serum albumin in phosphate buffered saline, cells were fixed, permeabilized, then incubated with 0.5 mL of Click-iT® Plus reaction cocktail for 30 min to detect Click-iT® EdU. Cardiac fibroblasts were resuspended in 1× Click-iT® saponin-based permeabilization and wash reagent and analyzed by flow cytometry.EdU incorporation experiment was conducted using Click-iTThe migratory ability of FGF-23-treated cardiac fibroblasts without and with the co-administration of PD166866 (1 μM), EGTA (1 mM), or U73122 (1 μM) was assessed using a wound-healing assay. Briefly, confluent cells were wounded using a P200 pipette tip 8 h before the endpoint of treatments in 6-well culture plates . Each gaCardiac fibroblasts were homogenized and lysed in a radioimmunoprecipitation assay buffer containing 50 mM Tris at pH 7.4, 50 mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 0.1% sodium dodecylsulfate (SDS), and a protease inhibitor cocktail (Sigma), as described previously . ProteinTotal RNAs isolated from cardiac fibroblasts were reverse-transcribed using SuperScript III reverse transcriptase . FGF receptor 1–4 mRNA expressions were analyzed through quantitative PCR by using the ABI PRISM7300 system  and SYBER Green (Applied Biosystems). Relative changes in the transcript levels of target genes were estimated from the threshold cycle (Ct) value and normalized to the respective Ct value of GAPDH (∆Ct) in corresponding samples.2+ imaging was performed as described previously [2) were loaded with fura-2-acetoxymethyl ester  and Pluronic F-127 (20% solution in 2.5 μg/mL dimethyl sulfoxide) in a Ca2+-free solution containing 120 mM NaCl, 5.4 mM KCl, 1.2 mM KH2PO4, 1.2 mM MgSO4, 10 mM glucose, and 6 mM HEPES (pH 7.40) for 30 min at 36 °C in a humidified incubator with 5% CO2. Fura-2 fluorescence images were captured using a Polychrome V monochromator  mounted on an upright Leica DMI 3000B microscope  with dual excitation wavelengths of 340 and 380 nm, and an emission wavelength of 510 nm. Fura-2 images were analyzed using MetaFluor software . The ratio of emitted fluorescence due to excitation at 340 nm (F340) to 380 nm (F380) was used as a marker of the relative intracellular Ca2+ levels. Cells were first exposed to the Ca2+-free solution, followed by ER Ca2+-ATPase inhibitor  co-treatment for ER Ca2+ store depletion to assess ER Ca2+ release. After the intracellular Ca2+ surge from ER Ca2+ leak induced by thapsigargin returned to a steady-state, the extracellular Ca2+ concentration was then increased to 2 mM to measure Ca2+ entry. Acute Ca2+ response to FGF-23 was assessed in the presence of extracellular Ca2+ (1.8 mM) without and with pretreatment of U73122 (1 μM for 5 min) or 2-APB (50 μM added 5 min prior to recording) [2+ release from external Ca2+ entry, cardiac fibroblasts were stimulated with FGF-23 (25 ng/mL) in a Ca2+-free condition, followed by restoration of extracellular Ca2+ concentration to 1.8 mM. The integrative (area under the curve) values of fluorescence intensities (F340/F380) corresponding to ER Ca2+ release and Ca2+ entry were calculated for analysis respectively.Caeviously . Cardiaccording) ,44,45. T3 measurement by using an ELISA kit  according to the manufacturer’s protocol. Data were normalized to protein concentrations and expressed as a fold change relative to control.The cell lysates of cardiac fibroblasts with and without FGF-23 (25 ng/mL) treatment for 24 h and 48 h were collected for the IPt-test. A p value of <0.05 was considered to indicate a statistically significant difference.All quantitative data are expressed as the mean ± standard error of the mean. Statistical significance in cardiac fibroblasts under different conditions was determined using a one-way repeated analysis of variance with a post hoc Tukey’s test and the paired or unpaired 3 signaling pathway, leading to an upregulation of Orai1 and/or TRPC1-mediated Ca2+ entry and thus enhancing the proliferative and migratory abilities of human atrial fibroblasts.FGF-23 activates FGF receptor 1 and subsequently PLC/IP


Little guidance exists regarding how best to upskill and support those delivering complex healthcare interventions to ensure robust trial outcomes and implementation fidelity. Mentoring was provided to occupational therapists (OTs) delivering a complex vocational rehabilitation (VR) intervention to stroke survivors. This study aimed to explore mentors’ roles in supporting OTs with intervention delivery and fidelity, and to describe factors affecting the mentoring process and intervention delivery.n = 6) and OTs (n = 19) explored experiences and perceptions of intervention training, delivery and the mentoring process. Mean total and monthly time spent mentoring were calculated per trial site. Qualitative data were analysed thematically.Quantitative data  was extracted from mentoring records and emails between mentors and OTs, alongside qualitative data on barriers and facilitators to intervention delivery. Semi-structured interviews with mentors (Forty-one OTs across 16 sites were mentored between March 2018 and April 2020. Most mentoring was provided by phone or Microsoft Teams (range: 88.6–100%), with the remainder via email and SMS (Short Message Service) text messages. Mentors suggested strategies to enhance trial recruitment, improved OTs’ understanding of- and adherence to trial processes, intervention delivery and fidelity, and facilitated independent problem-solving. Barriers to mentoring included OT non-attendance at mentoring sessions and mentors struggling to balance mentoring with clinical roles. Facilitators included support from the trial team and mentors having protected time for mentoring.Mentoring supported mentee OTs in various ways, but it remains unclear to what extent the OTS would have been able to deliver the intervention without mentoring support, or how this might have impacted fidelity. Successful implementation of mentoring alongside new complex interventions may increase the likelihood of intervention effectiveness being observed and sustained in real-life contexts. Further research is needed to investigate how mentors could be selected, upskilled, funded and mentoring provided to maximise impact. The clinical- and cost-effectiveness of mentoring as an implementation strategy and its impact on fidelity also requires testing in a future trial.ISRCTN12464275. Registered on 13th March 2018.ISRCTN, The online version contains supplementary material available at 10.1186/s12874-021-01382-y. Trialists are responsible for ensuring that those delivering trial interventions are adequately prepared and supported to implement them with fidelity to ensure robust trial outcomes  and optiMultiple strategies may be needed to support staff because barriers to delivering complex interventions with fidelity exist at organisational and practitioner levels , 6–8, alA novel approach to support intervention delivery was taken in the Facilitating Return to work through Early Specialist Health (FRESH) feasibility trial of vocational rehabilitation (VR) for traumatic brain injury survivors . A trainThe RETAKE trial aims to determine whether providing early stroke-specialist vocational rehabilitation plus usual NHS rehabilitation is more clinically and cost-effective for supporting post-stroke return to work than usual care (UC) alone . This coTo explore mentors’ roles in supporting OTs with intervention delivery and implementation fidelity.To describe factors affecting the mentoring process and intervention delivery.The qualitative sections of this paper are reported in accordance with the Standards for Reporting Qualitative Research (SRQR) guideline All six mentors had extensive experience delivering VR, often with focus on neurological conditions such as stroke or traumatic brain injury. This experience enabled reflection on times they had been in similar situations and use of those experiences to guide OTs.“And obviously I’ve got experience to draw on as well so that if people hadn’t, you know, I can say, “Well, I had something similar and I found that this worked.” [Interview with Mentor 1]The combination of mentors’ VR backgrounds and understanding of the trial, RETAKE intervention and OTs’ potential site-related challenges exemplifies cognitive participation because it suggests they were the right people to support delivery of the RETAKE intervention.In addition to training on the RETAKE mentoring process, mentors reported that attending training for OTs at trial outset improved their understanding of trial processes and the RETAKE intervention. This ensured coherence by facilitating shared understanding of the intervention’s aims, objectives and potential benefits. Attending the training also enabled mentors to meet OTs face-to-face and learn about their backgrounds and possible site-related challenges.“ … I’d never done that [teleconference mentoring]. So it was a little daunting for me and I had to sort of devise my own systems of how I recorded it and how it could be – how to recognise people’s voices … ” [Interview with Mentor 2]The adaptability of mentors and OTs in devising strategies to overcome initial challenges demonstrates collective action, through their work to operationalise the group teleconference format.Two mentors experienced initial difficulties knowing which OTs were speaking when delivering mentoring by teleconference. They developed strategies for overcoming this, such as asking OTs to say each other’s names when speaking, making notes per OT, and planning an agenda.n = 2), start new jobs in other Trusts or services (n = 6), or left due to pressures from their usual clinical role (n = 1). As Table Mentoring records and emails demonstrated 41 OTs across 16 trial sites participated in mentoring Table . It was Qualitative analyses of interviews and mentoring records and emails demonstrated that ad-hoc support was provided to OTs via phone, email and/or SMS text message. In contrast to data in Table “I think once a month is enough … But also, [mentor] has said that we can contact her in between. So if we've got something pressing, we can email her or have a conversation with her in between..” [OT 18 from site 10]Six of the 19 OTs reported that monthly mentoring sessions plus ad-hoc, additional support provided enough opportunity to engage with their mentor and other OTs.“And they [recruiters] never gave a thought to what happens about work because there was always another service that would deal with that in their minds. So asking them to think differently was quite a challenge.” [Interview with Mentor 2]Mentoring records and emails indicated that recruitment was sometimes paused because recruiting staff did not have capacity to recruit new participants. In these instances, cognitive participation and collective action were demonstrated because mentors worked with OTs to define strategies to increase and sustain trial recruitment, e.g., OTs prompting recruiters to increase efforts and offering help in recruiting. Following this communication and by involvement of Principal Investigators (PIs), recruitment increased. Other solutions included clarification of eligibility queries and encouraging OTs to obtain consent to follow-up even if in doubt of eligibility.Delays in referrals prevented OTs from delivering early intervention to participants. One mentor reported that recruiters in one site lacked understanding as to why stroke survivors with low level impairments needed support for returning to work, demonstrating lack of understanding of the aims of the trial and intervention  among recruiters.“I was like well, you know, it’s been agreed as part of the trial that you can in-reach and you can go and see people in A&E, and it just took a lot of sort of reassuring them (laughs) that they weren’t breaking any rules … .” [Interview with Mentor 2]Mentors supported OTs in developing coherence by helping them understand their tasks and responsibilities in relation to the trial. According to mentors, the sessions provided a safe place to confidentially discuss caseloads. Mentoring records demonstrated that contamination issues  were discussed in sessions and actions planned. For example, seeking advice from the trial team, or advising the OT on direct action.“We discussed how to avoid contamination and agreed; see the pt alone at the end of the session for voc [vocational rehabilitation] conversation. Educate the other therapists not to copy what has been said/done by the RETAKE OT and just note pt seen by RETAKE OT in standard notes.” Regular communication between OTs, mentors, and the trial team led to collective action through building of accountability and confidence in each other’s abilities .Across mentoring records and mentor interviews a key aspect of the mentors’ role at the trial outset included answering OT’s queries about trial processes, e.g., when and how to complete trial case reporting forms, writing letters to participants and employers. If mentors were uncertain they sought clarification from the trial team. One mentor described reassuring community-based OTs that they could visit participants in acute settings, ensuring participants were seen within trial timelines .“[RETAKE OTs] said they were writing letters after seeing pts but also writing continuation sheets... We discussed this and they agreed they could write ‘see letter’ on the continuation sheet … ” [Mentoring Record 21/11/18]Mentoring records/emails also showed that OTs reported they did not always have enough time to complete trial documentation. Cognitive participation was demonstrated when mentors worked with OTs to collectively define practical solutions to speed up and sustain trial documentation .“Therapist unclear what voc [vocational] support to offer at this point. Reassured ok still very early stages. Talked through establishing job demands, using job description/break down of job, relating to current activity, how to establish grade plan of activity at home to test out and build up skills.” [Mentoring Record 06/08/18]Mentors had extensive knowledge of resources to facilitate return to work after stroke. Mentoring records and emails indicated that many resources were provided by mentors to individual OTs, or to all OTs if seen as relevant to all. Collective action was evident with mentors advising OTs on use of these resources and signposting to relevant training to support their use.Four mentors stated that many OTs needed their support to apply newly acquired knowledge of VR, and both mentors and mentoring records indicated that OTs were sometimes unsure what VR treatment plans would look like. Mentors supported OTs in developing coherence by them on the tasks and responsibilities required when creating VR treatment plans.“One participant discharged after 2 weeks back at work, had spoken with manager who was happy with performance. Advised that I felt this was too early for discharge and that she [OT] should be establishing a mechanism for keeping in touch and follow up.” [Mentoring Record 20/11/18]Mentors also provided guidance on participants’ access to benefits, tailoring interventions according to clinical assessment results, and working with employers.“I think one of the common themes that comes up is around talking about how they [OTs] manage employers, or how they manage the interactions with employers, or employers who are not really engaging with them.” [Interview with Mentor 5]OTs frequently lacked confidence in working with employers as it was unchartered territory for many. In addition to managing difficult or non-engaging employers, mentors supported OTs’ progression to cognitive participation by advising them on employer expectations, ensuring clarity in the structure, content and expected outcomes of interactions, and tailoring their communication styles. Four mentors explained that they provided reassurance to OTs, that what they were doing- or had done was appropriate or, “on the right track.” Mentors demonstrated cognitive participation by supporting OTs in believing they were contributing to the RETAKE trial. Further evidence for this came from OTs reporting occasional checks with mentors that they were following the right steps with intervention delivery, and from mentoring records and emails.“You wanted to just to check you had thought of everything – which you had. Well done.” A common issue was that OTs were hindered from starting the RETAKE intervention early, and delivering it at the required intensity, duration, and dose. Across mentor interviews and mentoring records and emails, common reasons included staff shortages and pressures from UC workloads. Three mentors and several mentoring records and emails highlighted unsupportive management issues, leading to increased pressures on OTs to prioritise UC over RETAKE, refusal to let OTs take on any more RETAKE participants, and pressure to drop out of RETAKE altogether. In these instances, coherence and cognitive participation did not seem evident among managers; mentors responded by engaging in collective action to support OTs.“[RETAKE OT] had been told by their manager that their usual care caseloads takes priority as that was what they were commissioned to do but you rightly reminded them that the trust was being paid to carry out research so the RETAKE pts must be seen as prescribed.” OTs across two sites reported that where their managers had not been supportive it had been due to a change in management during the RETAKE study period, and the new managers not having had a formal introduction to RETAKE.… with my new manager who didn’t know anything about Retake, she kind of inherited our team, I had to explain it all to her and I am not sure, because she has just got so much to do, they have restructured and she has got loads of teams now … I am not sure if she ever really kind of understood what was going on with it … ” “ All 19 OTs interviewed demonstrated evidence of coherence, because they believed in the value of the RETAKE intervention. Their most frequently valued components including early intervention, case coordination and opportunity to build relationships between employers and participants. Ten OTs explained that mentoring facilitated intervention delivery through the advice and best practice examples shared. Interviews with mentors and mentoring records and emails indicated that mentors supported OTs in developing coherence by advising OTs on their tasks and responsibilities for delivering core components of the RETAKE intervention. Mentors also facilitated OTs’ adherence to the trial protocol during intervention delivery. For example, by advising OTs to start working with participants early  and ensuring OTs did not discharge participants as soon as they had returned to work.“I guess my role is to try and facilitate a discussion amongst the team where they can do some shared learning … it’s not about me giving answers but trying to facilitate a discussion to draw people in … ” [Interview with Mentor 3]OTs reported their learning was supported by this facilitation of meaningful conversations; seven agreed that mentoring sessions enabled problem-solving, through input from their peers and reassurance from the mentor.“ … if situations come up there are other people that can sometimes also give advice, you know, I tried this with my patient, or you can advise them on this or whatever...” Frequent liaison with other RETAKE OTs at the same site  was beneficial. Mentoring records showed that OTs within sites (and sometimes across sites) met regularly for peer support and visited participants together when an OT lacked confidence. OTs across 7 of the 11 sites represented in the interviews reported they were able to regularly gain peer support from site partners outside of mentoring sessions. Three of the 19 OTs interviewed reported that regular contact with their site partner provided a safety net and opportunity to talk freely about participants.“I work really closely with my other counterpart that’s doing the RETAKE. Having somebody else has been really, really valuable because you can’t talk to anyone at work about it. You don’t want to bug people sort of from RETAKE all the time.” In contrast, four OTs with little contact with site partners reported feeling isolated.“I still feel quite alone in it all, I know [site partner] is there if need be, having said that we have been trying to communicate all day yesterday and failed.” Overall, regular communication between mentors and OTs (including other OTs at the same site or different sites) enabled collective action because it provided opportunity to discuss caseloads and conduct joint visits, thus building accountability and confidence in intervention delivery and supporting local operationalisation of RETAKE intervention delivery.Cognitive participation was evident because five mentors reported encouraging OTs to independently problem-solve, with mentors providing guidance where necessary. All OTs in the session were encouraged to share advice to facilitate peer support.“I’ve always tried to keep the mentoring on a Monday because that’s the day I didn’t work … I certainly didn’t find it difficult if OTs contacted me to say, could we have a phone call?” [Interview with Mentor 5]For mentors, the most important facilitator to the mentoring process was support from the RETAKE trial team, including two mentors who were also members of this team. Four mentors reported receiving support with administration, trial processes and/or mentoring itself . Two mentors also mentioned time availability as a facilitator to the mentoring process, as being retired or working part-time enabled them to provide support when necessary.“Monday was the only day that worked for all the people in each group as it were … the days where it was a challenge was where I had [home] visits … I would be in the car and I did one or two mentoring sessions on my phone in the car, just park up somewhere, and then went off to my next visit.” [Interview with Mentor 6]A common barrier to the mentoring process was non-attendance of OTs at mentoring sessions; this was reported by three mentors as well as mentoring records and emails. Mentors’ perceived reasons for non-attendance included OTs having competing priorities in their usual caseloads, OTs needing to cover for colleagues on maternity- or sick leave, a rigid ‘clocking off’ mentality preventing OTs from attending sessions outside of their working hours, and a lack of confidence in participating in sessions and/or delivering the intervention. One mentor put in additional effort to encourage an OT to attend mentoring, as evidence from her competency assessment suggested she might not fully understand her RETAKE OT role.“I’ve got a new person who hasn’t come into mentoring so I’ve just emailed them now to say, “Can we have a ten minute conversation?” … She thinks she knows what she’s doing but I’ve just marked her paper [written competency assessment] and she doesn’t … ” [Interview with Mentor 4]In contrast, two mentors found it difficult to juggle mentoring with their usual work, leaving them “overwhelmed” as one mentor put it. Another reported having to conduct some mentoring sessions via phone in her car between home visits, and she found it difficult disengaging from one work activity to the next.“whether we look at training needs because … as they [OTs] get into the process of the RETAKE, that’s when they’re recognising the skills lack in certain areas and whether we can offer any form of training” [Interview with Mentor 5]Two of the 19 OTs felt there was insufficient time to discuss caseloads in a 60-min mentoring session; and felt the time could have been structured more efficiently. Four OTs felt mentoring would work better in group format, whilst three others felt it would work better as one-to-one sessions between mentors and OTs.Suggestions for improving mentoring related to how it was organised, delivered, and evaluated. The most common suggestion from mentors was that it should occasionally occur face-to-face, because it was easier to detect more when communicating this way. Other suggestions included arranging mentoring by site, mentors receiving feedback from OTs, formal evaluation of OTs’ letter-writing, and meetings between mentors to share experiences and discuss OTs’ training needs.“But for me, if a trial is introducing a new way of working with therapists that involves a complex intervention, I would say it’s extremely valuable to have a mentoring system in place.” One mentor expressed concern that if mentoring was not rolled out alongside similar complex interventions in NHS settings, the pressures of new referrals would result in the intervention becoming “watered down.” She stated that adequate mentoring support would be needed to ensure those delivering the intervention did not withdraw too soon and were equipped to handle novel situations and challenges. Half of the mentors explained that how mentoring would be set up and paid for, and who would be suitable to provide mentoring within the contexts of local organisations were key for consideration in future trials.“the likelihood is that there are going to be local issues that have to be solved. So, either somebody externally has to come in and champion those and try and sort those out, or you have to have internal champions who are willing to think about those implementation issues and look at various different ways that they can be solved from within the organisation.” [Interview with Mentor 1]Due to the large number of OTs potentially delivering the intervention at any given time, four OTs suggested a future regional hierarchy based on skillset, whereby the more experienced OTs train and support the less experienced OTs. Four OTs also suggested they themselves could act in a mentoring/supervisory role.Among OTs, mentoring was considered essential in supporting complex intervention implementation in a trial context. Fourteen of the 19 OTs interviewed stated that a mentoring system would be needed alongside future roll-out of a similar VR intervention.Little guidance exists regarding how best to upskill and support those delivering complex interventions to ensure implementation fidelity and robust trial outcomes. In the UK, adequate trial-focused training and support for NHS staff may mean trial findings are more contextually relevant and translate more easily into real-world clinical practice. This study examined mentoring provided to OTs delivering a complex VR intervention to stroke survivors in NHS settings. The aims were to explore mentors’ roles in supporting intervention delivery and implementation fidelity; and to explore factors affecting the mentoring process and intervention delivery.Mentors’ roles included supporting OTs with coherence, cognitive participation, and collective action through advising them on adherence to core components of the RETAKE intervention, such as commencing intervention early, individual tailoring, communicating with stakeholders, and avoiding discharge of participants immediately following return to work . Over half of OTs interviewed felt that mentoring facilitated their intervention delivery through provision of advice and best practice examples, suggesting that they valued mentors’ expertise and saw them as opinion leaders in VR. Similarly, in a Cochrane review  health pMentors also supported OTs with coherence and cognitive participation, through advising on trial processes relating to trial documentation completion, contamination, and recruitment. Support with trial documentation completion appears a novel finding in research, noteworthy because complicated trial documentation has been cited a key barrier to clinicians participating in practice-based research , 42. IncBarriers to delivering the RETAKE intervention with fidelity included heavy usual caseloads and staff shortages. Similar issues were reported as barriers to nurses’ delivery of a family violence screening and care model . The autRETAKE mentors were funded through research budgets, but to ensure successful future roll-out alternative methods of resourcing this support would require consideration. Some OTs suggested a regional hierarchy based on skillset, whereby more experienced OTs train and support less experienced OTs. If the experienced OTs were considered opinion leaders their input could facilitate cost-effective intervention delivery, where funding for mentoring could be offset by savings through effective implementation. Such an approach would require justification and testing through implementation research .Another point to consider is how future suitable mentors would be selected and upskilled. RETAKE mentors received training on the mentoring process and benefitted from attending RETAKE intervention training to improve their understanding of training, trial processes, the intervention, and OTs’ backgrounds. It is uncertain how this training would be funded and delivered within or across local contexts. The Royal College of Occupational Therapists (RCOT) trained health and work champions  to deliver training to other staff within local organisations, enabling them to deliver brief VR advice to patients wishing to return to work . It is pThis research had multiple strengths. Firstly, the thematic analysis of qualitative data in this study followed a systematic multi-step approach and included standard measures in order to increase rigour and trustworthiness.The use of NPT as a sensitising framework for interpreting findings is considered a strength, as it offers insight into the mechanisms that may affect implementation of this intervention in the future.We acknowledge that the sample of mentoring records and emails which were analysed covered a set time period which, although large, did not include data from later stages of the trial such as issues around closing sites or impact of the COVID-19 pandemic. The sample size of 6 mentors and 19 OTs can be considered small in relation to the size of the RETAKE trial. However, it included all of the mentors and at least one OT from each site and from different time points in the intervention delivery process. These recruitment methods, using purposive sample, together with the methods of analysis and use of NPT suggest that this sample generated data which are likely to be transferable to similar studies and settings .Little guidance exists on how best to support those delivering complex interventions to ensure robust trial outcomes and implementation fidelity. Study findings indicated that RETAKE mentoring enhanced trial recruitment, improved OTs’ understanding of- and adherence to trial processes, supported implementation fidelity and intervention delivery, and aided OTs’ development of problem-solving skills. It remains unclear to what extent RETAKE OTS would have been able to deliver the intervention without mentoring support or how this might have impacted implementation fidelity. Successful implementation of mentoring alongside such interventions may increase the likelihood of intervention effectiveness being observed and sustained in real-life contexts. Further research is needed to investigate the benefits of this approach for other complex interventions, including how mentors could be selected, upskilled, funded and mentoring provided to maximise impact. To ensure those implementing new complex interventions into clinical settings invest in the whole package, the clinical- and cost-effectiveness of mentoring as an implementation strategy and its impact on fidelity require testing in a future trial.Additional file 1.Additional file 2.Additional file 3.Additional file 4.


The diagnostic accuracy of the Compass database-guided analysis was evaluated in a prospective validation study (125 suspected AML patients). This method excluded AML associated with the following genetic abnormalities: t, t, inv(16), and KMT2A translocation, with 92% sensitivity [95% confidence interval (CI): 78.6%–98.3%] and a 98.5% negative predictive value (95% CI: 90.6%–99.8%). Our data showed that the Compass database-guided analysis could identify phenotypic differences between AML groups, representing a useful tool for the identification of DfN patterns.Acute myeloid leukemias (AMLs) are hematologic malignancies with varied molecular and immunophenotypic profiles, making them difficult to diagnose and classify. High-dimensional analysis algorithms might increase the utility of multicolor flow cytometry for AML diagnosis and follow-up. The objective of the present study was to assess whether a Compass database-guided analysis can be used to achieve rapid and accurate diagnoses. We conducted this study to determine whether this method could be employed to pilote the genetic and molecular tests and to objectively identify different-from-normal (DfN) patterns to improve measurable residual disease follow-up in AML. Three Compass databases were built using Infinicyt 2.0 software, including normal myeloid-committed hematopoietic precursors ( Acute myeloid leukemia (AML) refers to a heterogeneous group of malignant diseases characterized by the accumulation of aberrant hematopoietic progenitor cells, known as AML blasts, that cannot progress beyond various stages of maturation and are unable to develop into mature blood cells.in situ hybridization (FISH)) and molecular analyses [polymerase chain reaction (PCR) and next-generation sequencing (NGS)].According to the current World Health Organization (WHO) criteria , an AML At present, multicolor flow cytometry (MFC) is viewed as a complementary tool that can assist with the AML diagnostic process. MFC is typically used to define the blast cell lineage and can be used to identify phenotypic aberrations, known as leukemia-associated immunophenotypes (LAIPs), such as the presence of aberrant lymphoid markers, maturation asynchrony, or the absence of myeloid markers, which might be useful for assessing measurable residual disease (MRD) during AML treatment follow-up.The evaluation of cytogenetics and mutational profiles represent reference methods for monitoring MRD in AML, allowing for the assessment of clonal evolution and the stratification of AML into prognostic subgroups to guide treatment approaches . DespiteHowever, despite the high applicability of MFC for MRD assessments in AML patients  compared with molecular MRD assessments , multiceTo improve AML MRD detection by MFC, the ELN MRD Working Party has recommended combining the different-from-normal (DfN) approach with the LAIP assessment method . The majNew tools for MFC data analysis have recently been developed to objectively visualize immunophenotypic differences between abnormal cells from different pathologies. One such tool is Infinicyt Compass, which was developed by the EuroFlow™ (EF) Consortium, and allows for the recognition of complex immunophenotypic patterns through multivariate analyses of flow cytometric data.The present study aimed to assess whether the Compass database analysis could be used to guide the genetic and molecular testing of AML to achieve a rapid and accurate diagnosis and to perform DfN analyses.The results of this study showed that the comparison of new cases against reference databases composed of well-classified AML cases represents a user-friendly method that can facilitate the orientation of genetic and molecular biology testing to achieve a rapid, accurate diagnosis. In addition, the Compass database-guided analysis of MFC data can be used as a nonsubjective method for DfN evaluation.The present study was conducted in three phases: construction of Compass databases; databases-guided analysis of new AML cases at initial diagnosis; and evaluation of database-guided DfN analysis Figure 1t AML (n = 8), t AML (n = 19), inv(16)/t AML (n = 12), and AML with MLL gene translocations ; all samples were diagnosed by the Hematology Laboratory from the University Hospital of Saint-Etienne between 2013 and 2019. Normal myeloid hematopoietic precursor cells (my-HPCs) committed toward a neutrophil lineage (CD45+low/int CD117+ CD34+/− HLA-DR+int CD13+ CD14− IREM2− CD33+ CD36−), a monocyte lineage (CD45+low/int CD117+ CD34+/− HLA-DR+hi CD64+ CD14− CD300e[IREM-2]− CD13+ CD33+), and an erythroid lineage (CD45−/+low CD117+ CD34+/− HLA-DR+low/int CD36+ CD105+ CD33− CD35+low) were also included , CD14+low granulocytes and CD14+ IREM-2+ monocytes (tube 2), and CD10+ hematogones (tube 1) , including included Figure 2 = 8), t1;17 AML (19), inv1/tEach group of cases was subsequently plotted in a balanced automatic population separator (APS), a principal component analysis (PCA) plot for comparisons with other groups included in the databases.The BM samples used in this prospective study were obtained between January 2019 and June 2021 from 125 consecutive patients who were hospitalized with suspected AML at the Institut de Cancérologie Lucien Neuwirth, Saint-Priest-en-Jarez, the Estaing University Hospital of Clermont-Ferrand, France, and the Oncological Institute Prof. Dr. Ion Chiricuță, Cluj-Napoca, Romania.Written informed consent was obtained from each patient and healthy donor (HD), as approved by the institutional procedures of the independent ethics committee and the Comité de Protection des Personnes - Ile de France (NCT03233074/17.07.2017).All participants’ characteristics are summarized in The phenotypes of leukemic blasts obtained from new patients were compared against the phenotypes of AML blasts from four different cytogenetic groups and the most similar my-HPCs using the three databases.+low expression, CD117, HLA-DR positivity, and side scatter (SSC) characteristics, as previously described (+low granulocytes and CD14+ IREM-2+ monocytes (tube 2), and CD10+ hematogones (tube 1) (AML blasts were selected according to CD45(tube 1) Figure 3The AML blast events identified in each individual AML case were compared with each well-classified AML group and normal my-HPC populations using balanced APS plots.The similarity between any two populations was scored based on the position of the median for the new AML blast events from each case relative to the 1 and 2 standard deviation (SD) curves for the AML groups and normal my-HPCs included in the database: falling within 1 SD was scored as 1 point  PCA-based plot.MLL-r AML, eight time points for one with t AML, four time points for one with t AML, and six time points for one with inv(16) AML. Nondebris, singlet, CD117+ HLA-DR+ my-HPCs were selected from each of the fcs files analysed for each patient, as described above , AML inv(16), AML t, and MLL-r AML .All samples were stained using the first three tubes of the EF AML/MDS antibody panel . Sample Appropriate instrument performance was confirmed by performing FranceFlow and EuroFlow quality assessments , 10.At least 100,000 BM cells/tube were acquired using a three-laser, eight-color BD FACSCanto-II™ flow cytometer  at each study site. Acquired cells were then analyzed using Infinicyt V2.0 .May-Grünwald-Giemsa-stained BM aspirate smears from each AML patient were examined under a light microscope by experienced pathologists and the diagnosis was established conforming WHO 2016 guidelines  analysis at the time of diagnosis. Karyotypes were analyzed after 24 h of unstimulated culture using standard procedures. The chromosomes were stained by R- and G-banding. At least 20 metaphase events were analyzed. The results were interpreted and reported according to the International System for Human Cytogenetic Nomenclature  .PML-RARA; dual-color, dual-fusion probe, Abbott, Des Plaines, IL, USA), Runt-related transcription factor/RUNX1 partner transcriptional corepressor 1 , core-binding factor subunit beta , and lysine methyltransferase 2A . Detection was performed on freshly harvested BM cells (metaphase and interphase). Twenty metaphase events and 200 nuclei were observed for each case.FISH was performed for promyelocytic leukemia/retinoic acid receptor α , CBFB-MYH11 variant A, and CBFB-MYH11 variant D. In brief, extracted RNA was analyzed by RT-PCR for the PML/RARA, RUNX1-RUNX1T1 (AML1-ETO), and CBFB-MYH11 fusion transcripts on an ABI HT platform  and on an ABI 3500 DNA analyzer . Quantitative values are expressed as a ratio of the fusion transcript level to the ABL1 transcript level (%), and the sensitivity was determined to be 0.001%.Multiparametric RT-PCR was performed to detect recurrent fusion transcripts in all newly diagnosed AMLs. In cases positive for any fusion products, qRT-PCR was performed to monitor treatment response. The panel tests included WT1) expression levels were quantified in PB samples using qRT-PCR and normalized against total ABL1 gene expression levels to monitor MRD for KMT2A(MLL)-rearranged AML cases.Wilms’ tumor gene , paired-end sequencing was performed on an Illumina MiSeq System , and sequence analysis was realized on an outsourced bioinformatics solution SeqOne .A receiver operator characteristic (ROC) curve was generated to determine the ability of the Compass database-guided analysis to correctly classify new AML cases based on the established scoring system for this study.The ROC curve, area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and likelihood ratios were estimated using MedCalc Software Ltd. Version 19.8 .Materials and Methods section, is shown in The resulting AML databases and the database-guided analysis tool were validated on 125 consecutive AML-suspected cases. The results were compared with the final diagnosis according to WHO 2016 guidelines that were established by each center. Overall, the distribution of the 125 cases across the various AML groups was as follows: 15 (12%) t AML; 7 (5.6%) t AML; 6 (4.8%) inv(16)/t AML; 8 (6.4%) KMT2A(MLL) AML; 88 (70.4%) other AML; and 1 (0.8%) non-AML. The distribution across the database-guided result categories , based on the scoring algorithm described in the p < 0.001; t, t, inv(16)/t, and KMT2A(MLL) translocation with 92% sensitivity (95% CI: 78.6%–98.3%), a 98.5% negative predictive value (95% CI: 90.6%–99.8%), and a likelihood ratio for a negative test of 0.04 (95% CI: 0.01–0.28). The negative predictive value was 100% for t and t AMLs; and 99.1% (95% CI: 95.2%–99.9%) for inv(16)/t and KMT2A(MLL) AMLs (95% CI: 94.6%–99.9%). Globally, a reduced positive predictive value (PPV) of approximately 58% (95% CI: 49%–66%) was observed .When combining the Compass database-guided results obtained from the first three tubes of the EF AML/MDS panel, a true positive result was obtained in 33 (26.4%) cases, a true negative result was obtained in 67 (53.6%) cases, a false-positive result as obtained in 24 (19.2%) cases, and a false-negative result was obtained in one (0.8%) case. The Compass database-guided diagnosis allowed for correct classification with an AUC of 0.83 : 0.75–0.89; t AML (9/118 negative cases), inv(16)/t AML (6/119 negative cases), and KMT2A(MLL) AML (8/117 negative cases).A relatively increased number of false-positive cases were observed for the following AML groups: t AML cases with the typical t AML cases on the APS plots  AML group), the NAPS diagrams  AML cases and these false-positive t AML cases (except AML case 35) for markers from tube 1 of the EF AML/MDS panel, based on different patterns for HLA-DR (26%), and CD13 (25%) and differences in the SSC (26%) and forward scatter  parameters  AML cases, based on differences in CD33 and SSC  AML group were found to harbor a somatic mutations clustering within the Runt domain  AML blasts.Despite the perfect correspondence between the phenotypes of six discordant diagrams  enabled diagrams Figure 6When examining the false-positive inv(16)/t AML cases, the NAPS diagram  AML phenotype. The Compass database-guided analysis guided the diagnosis toward the MLL-r AML group. Genetic and molecular tests confirmed the absence of t or PML/RARA rearrangement; however, the karyotype and FISH analyses did not reveal the presence of an MLL rearrangement, and an array-comparative genomic hybridization (CGH) analysis was performed. A PICALM/MLLT10 (CALM-AF10) fusion gene was identified.An interesting example from the t AML by the Compass database-guided analysis of the MFC results  and FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD). An APL‐like immunophenotype was previously described for AML with mutated NPM1, associated with significantly longer relapse‐free survival compared with patients lacking this phenotype  was transferred from a peripheral hospital to the University Hospital of Saint-Etienne with suspicion of APL. The hemostasis screening tests revealed a decrease in the prothrombin time and hypofibrinogenemia. Complementary tests were performed with urgency on PB and BM aspirate samples. Despite a preliminary orientation toward henotype  and a gohenotype .The discriminatory power to distinguish between the different AML groups and normal my-HPCs included in the databases was assessed for the performance of DfN analysis using the Compass database-guided analysis. Tested antibody combinations were evaluated using balanced APS plots, using all eight markers and the FSC and SSC parameters for each tube of the EF AML/MDS panel Figure 8t AML group. Interestingly, a good separation was also observed between t AML blasts and normal neutrophil-committed CD13+ HPCs (tube 1 of the EF AML/MDS panel), normal monocyte-committed CD64+ HPCs (tube 2 of the EF AML/MDS panel), and normal erythroid-committed CD36+ CD71+ CD33− HPCs (tube 3 of the EF AML/MDS panel). For tube 1 of the EF AML/MDS panel, the first principal component  showed the major contributions of the SSC (19%) and FSC (17%) parameters, whereas the second principal component  showed the major contributions of HLA-DR (44%), CD34 (19%), and CD13 (10%) for the separation of the t AML group from normal neutrophil-committed HPCs and all other AML groups.Antibody combinations from the three tubes only provided a clear distinction between the blasts from different AML groups was for the t AML blasts, normal monocyte-committed HPCs, and blasts from other AML categories. For tube 3 of the EF AML/MDS panel, PC1 indicated that the most useful parameters for group separation were FSC (13%) and SSC (11%), whereas PC2 showed that the best discriminating factors for tube 3 of the EF AML/MDS panel were HLA-DR (32%), CD34 (21%), and CD33 (13%), allowing for good separation between t AML blasts, normal erythroid-committed HPCs, and blasts from other AML categories.For tube 2 of the EF AML/MDS panel, the most discriminating markers identified in PC1 were CD64 (18%), FSC (14%), and SSC (12%), whereas PC2 included HLA-DR (43%) and CD34 (21%), resulting in the clear distinction between t AML cases; the coexpression of CD19 and CD56 was observed in 28% of t AML cases; the partial and low expression of CD19 was observed in 42% of t AML cases; CD4 expression was observed in 7% of inv(16)/t AML cases and in 18% of MLL-r AML cases; and CD7 expression was observed in 9% of all AML cases. Thus, performing MRD follow-up using these phenotypic aberrancies would only be applicable to a limited number of AML cases.In our study, the aberrant expression of lymphocytic markers on AML blasts was observed in a small number of cases: CD56 expression was observed in 13% of Therefore, we evaluated the utility of applying the Compass database-guided analysis based on the first three tubes of the EF AML/MDS panel to the detection of residual blasts in four AML cases harboring the most frequently detected genetic abnormalities.PML-RARA fusion transcript of 10−4 was more sensitive, and a difference of 1 log10 was noticed between the qRT-PCR and those obtained for the MRD evaluation by MFC  AML case  AML case because the isolation of events corresponding to residual blasts required the evaluation of multiple clusters on t-distributed stochastic neighbor embedding (t-SNE) graphs. In addition, in this case, we noticed discordances between the results obtained using the different EF AML/MDS tubes at the same time points  subgroups, which have been used for AML classification since 1976 .However, relatively little is known regarding the associations between genetic alterations and distinct immunophenotypic profiles.A lack of consensus regarding the choosing of AML immunophenotyping panels and data analysis strategies represent major drawbacks for AML classification. A collaborative effort within the EF consortium led to the development of the EF AML/MDS panel, which allows for the unequivocal identification of AML blasts and lineage assignments and the accurate evaluation of myeloid lineage maturation profiles , 9, 10. Novel software for MFC data analysis has been developed by the EF group, which includes several analysis tools to facilitate phenotypic comparisons between pathological cells derived from different groups of diseases, with the ultimate aim of facilitating the performance of fast, objective, and reproducible diagnostic assessments. A database-guided analysis of MFC outcomes is capable of objectively analyzing all single leukemic events and classifying each event individually, providing a global image of the composition of the leukemic bulk, which can frequently be heterogeneous in AML . This meThe Compass database-guided analysis, which is based on a PCA algorithm, has been demonstrated to be effective in various studies, such as for acute leukemia orientation , B‐cell In this study, we sought to evaluate whether this method and the antibody combinations provided by the first three tubes of the EF AML/MDS panel could be used to identify the most frequently identified recurrent genetic abnormalities in AML to rapidly orient cytogenetic and molecular tests and highlight the DfN characteristics of AML blast immunophenotypes.p < 0.001). The major advantage of this method was the ability to exclude certain recurrent genetic abnormalities from new AML cases, with 92% sensitivity and a 98.5% negative predictive value, which can prevent the performance of unnecessary, expensive, and time-consuming tests.Using the algorithm described here, the Compass database-guided diagnosis enabled the correct classification of AML types with an AUC of 0.83  AML cases with low levels of MRD (22). In line with this study (22), we observed that the MFC method showed a constant low level of MRD positivity before relapse in the t AML case, unlike the qRT-PCR method that indicated MRD levels below 10−4 at three different time points.In addition, previously published data show that qRT-PCR tests failed to predict disease progression in t AML and inv(16) AML cases.A good correlation was also observed between MRD evaluation by qRT-PCR and MFC in These preliminary results show that MRD evaluation in AML using MFC combined with Compass database-guided analysis should be considered to complement molecular biology for MRD detection.+low gate, limiting the specificity and sensitivity of this method.The limited number of antibodies that can be combined into a single tube for routine MFC diagnostic purposes is a disadvantage of this method, as a relatively high number of unclassified events remains in the CD45However, the findings of this study require large-scale validation in a multicenter study to gather sufficient quantities of sample data to establish a robust analytical model that can contribute to the development of supervised machine learning techniques capable of performing automated MFC interpretation for the objective detection of MRD in AML.In conclusion, the first three tubes of the EF AML/MDS panel, combined with the Compass database-guided analysis:allows for the exclusion of frequent recurrent genetic abnormalities in new AML cases, with 92% sensitivity and a 98.5% negative predictive value, which can contribute to preventing the performance of unnecessary, expensive, and time-consuming tests;t, t, inv(16)/t, and MLL-r AML groups with an AUC of 0.83 ;allows for the correct classification of MLL-r AML;provides valuable clues and can direct the extensive genetic or molecular exploration of difficult cases, such as NPM1+ AML cases that have an APL-like phenotype and may benefit from ATRA therapy; andallows for the identification of allows for the identification of DfN patterns, which may be useful for advancing MRD evaluations in AML.The original contributions presented in the study are publicly available. This data can be found here: FlowRepository, ID: FR-FCM-Z3JL.Written informed consent was obtained from each patient and healthy donor (HD), as approved by the institutional procedures of the independent ethics committee and the Comité de Protection des Personnes - Ile de France (NCT03233074/17.07.2017). The patients/participants provided their written informed consent to participate in this study.Conceptualization: C-MA. Formal analysis: C-MA, RV-M, CS, MM, AT, LR, PF-G, AS, and MC. Validation: C-MA, RV-M, CS, MM, AT, LR, PF-G, AS, and MC. Clinical investigation: ET, DG, ad CT. Data curation: C-MA. Writing—original draft preparation: C-MA. Writing—review and editing: C-MA, RV-M, CS, MM, AT, LR, PF-G, AS, MC, ET, CT, LC, and DG. All authors have read and agreed to the published version of the manuscript.Funding was provided by the Association Les Amis de Rémi (France).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


With the extracellular Ca2+ in the millimolar range, there is a steep Ca2+ gradient across the plasma membrane (PM). Thus, injury that damages PM, leads to a cytosolic Ca2+ overload, which helps activate PM repair (PMR) response. However, in order to survive, the cells must cope with the Ca2+ overload. In a recent study  we have examined how cells cope with injury-induced cytosolic Ca2+ overload. By monitoring Ca2+ dynamics in the cytosol and endoplasmic reticulum (ER), we found that PM injury-triggered increase in cytosolic Ca2+ is taken up by the ER. Pharmacological inhibition of ER Ca2+ uptake interferes with this process and compromises the repair ability of the injured cells. Muscle cells from patients and mouse model for the muscular dystrophy showed that lack of Anoctamin 5 (ANO5)/Transmembrane protein 16E (TMEM16E), an ER-resident putative Ca2+-activated chloride channel (CaCC), are poor at coping with cytosolic Ca2+ overload. Pharmacological inhibition of CaCC and lack of ANO5, both prevent Ca2+ uptake into ER. These studies identify a requirement of Cl- uptake by the ER in sequestering injury-triggered cytosolic Ca2+ increase in the ER. Further, these studies show that ER helps injured cells cope with Ca2+ overload during PMR, lack of which contributes to muscular dystrophy due to mutations in the ANO5 protein.Cells maintain their cytosolic calcium (Ca Studies since then have identified that entry of extracellular Ca2+ into the injured cells trigger vesicle fusion, membrane remodeling, and cytoskeletal reorganization, all of which are required to repair the PM of the injured cell. While it is well recognized that excess cytosolic Ca2+ causes cell death, there is little understanding of how cells handle the large amount of extracellular Ca2+ that enters the injured cell. Being the largest membrane bound organelle and the major site for storing intracellular Ca2+, the ER is likely to aid in coping with the cytosolic Ca2+ overload. In a recent study, we used an ER-targeted Ca2+ sensor and found that ER responds to PM injury by rapidly sequestering the extracellular Ca2+ that enters the injured cell. This uptake of Ca2+ by the ER was mediated by the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) – the ER resident pump required for cytosolic Ca2+ uptake into the ER. Inhibition of SERCA activity in injured cells through a pharmacological inhibitor (thapsigargin), prevented ER Ca2+ uptake. Concomitantly, this also impaired the ability of the injured cells to sequester the cytosolic Ca2+, and impaired PMR. While this identified the role of ER in the injured cell's ability to cope with Ca2+ influx, it raised two additional questions:It was nearly a century ago that Lewis Victor Heilbrunn identified the requirement of extracellular Ca2+?What is the mechanism by which ER senses and buffers increased cytosolic Ca2+ result in poor repair of the injured cell?Why does failure to buffer cytosolic Ca2+ level. Instead, sustaining SERCA-mediated ER Ca2+ uptake requires the lumen of the ER to be kept electroneutral through the export of H+ and import of negatively charged ions such as Chloride (Cl−). Previously, we had observed that cells from Limb Girdle Muscular Dystrophy 2L (LGMD2L) patients, repair poorly due to lack of an ER-localized putative Ca2+ activated chloride channel (CaCC) protein - Anoctamin 5 (ANO5) also called Transmembrane 16E (TMEM16E). If injury-triggered increase in cytosolic Ca2+ activates ANO5-mediated pumping of Cl- ions into the ER, then this could be the mechanism for the activation of Ca2+ uptake by the ER in healthy cells. In our recent study we found that similar to thapsigargin-treated healthy cells, LGMD2L patient cells are also poor at coping with injury-triggered increase in the cytosolic Ca2+. Through the use of a ER-targeted anion sensor, our study identified that PM injury led to anion uptake into the ER, which is compromised by NPPB (5-nitro-2-(3-phenylpropyl-amino) benzoic acid) - an inhibitor of CaCC. Similar to NPPB treated healthy cells, anion uptake into the ER was compromised in the injured LGMD2L patient cells. Concomitantly, the ER of the NPPB treated healthy cells and of the LGMD2L patient cells were both compromised in their ability to uptake injury-triggered increase in cytosolic Ca2+. By using a ANO5 knockout mouse, we examined Ca2+ homeostasis in injured ANO5-deficient muscle fibers, which identified that ANO5-deficient fibers are compromised in their ability to cope with the injury-triggered Ca2+ overload and in repairing PM damaged by focal injury or mechanical activity. Together, above results identify that cytosolic Ca2+ increase in injured cells triggers ER-resident CaCC activity, which supports clearance of cytosolic Ca2+ by the ER . This mechanism is the basis for the ER Ca2+ uptake in the injured cells and ANO5/TMEM16E is the CaCC that supports ER-mediated clearance of cytosolic Ca2+ overload in injured muscle cells.The answer to the first question is confounded by the fact that SERCA activity is not regulated by the cytosolic Ca2+ increase for PMR, the next question was why does failure to rapidly buffer cytosolic Ca2+ compromise the ability of the injured cell to repair. The answer came from examining (A) Annexin-mediated PMR, and (B) Mitochondria-mediated PMR. Based on the Ca2+ affinity of the different annexins, PM injury causes sequential recruitment and clearance of annexins at the injury site. This injury-triggered dynamics of annexins is required for remodeling and repair of the injured PM. ANO5 deficit in the LGMD2L patient cells altered the injury-triggered dynamics of two of the annexin proteins we examined - Annexin A1 and Annexin A2. While both these annexins were recruited to the site of injury in patient cells at the kinetics similar to the healthy cells, prolonged cytosolic Ca2+ increase in patient cells correlated with the failure of annexins to clear from the injury site, interfering with annexin-mediated PMR. Another PMR pathway is mediated by the injury-proximal mitochondria, which helps clear some of the cytosolic Ca2+, but also get depolarized and increases reactive oxygen species (ROS) production. ROS from these mitochondria then activates local buildup of actin cytoskeleton that helps close the PM wound. Injury of patient cells lacking ANO5 increased Ca2+ uptake and caused extensive depolarization of their mitochondria, impairing mitochondria-mediated PMR. Based on these findings we concluded that failure of ER to sequester injury-triggered increase in cytosolic Ca2+ impairs spatial and temporal regulation of PMR response, which compromises the ability of these cells to repair. This indicated that approaches to restore cytosolic Ca2+ balance in the injured ANO5 deficient cells could improve their repair ability. Accordingly, our study identified improved repair of ANO5 knockout muscle fibers treated with a cell permeant Ca2+ chelator - BAPTA-AM  ethane-N,N,N9,N9-tetraacetic acid-acetoxy-methyl ester).Given the requirement of Ca2+ increase, such that too little or too much increase both compromise PMR. It shows that ER-mediated cytosolic Ca2+ uptake allows the control over the amplitude and time of cytosolic Ca2+ increase in the injured cells. It highlights that Ca2+ uptake by the ER in injured cell relies upon Ca2+-activated anion transport and failure of anion or Ca2+ uptake by the ER disrupts the fine balance of multiple PMR repair responses mounted by the cell, compromising the cell's repair ability. Identification of this ER-mediated Ca2+ and PMR regulation not only identifies a new role for this organelle, but also offer insights into how defect in a putative anion channel can cause poor muscle repair in LGMD2L patients and point to future avenues for its therapy.Together, these results highlight the Goldilocks-like nature of injury-triggered Ca


Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely “Ising-type” methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. •Ising-type algorithms perform better with small datasets•There is less overfitting with Ising-type algorithms•Ising-type algorithms are amenable for drug discovery•Ising-type algorithms are useful for assessment of drug efficacy Advances in sequencing technology, leading to an ever-increasing volume and variety of data, present an opportunity to probe the molecular underpinnings of disease. Inspired in part by recent developments in physical quantum processors, we evaluated several Ising-type algorithms, which are relatively unused in the biomedical sciences, on actual human tumor data from The Cancer Genome Atlas. Our results show performance competitive with conventional machine-learning algorithms in classifying human cancer types and associated molecular subtypes when training with all available data; but perhaps more strikingly, the Ising-type algorithms demonstrate superior performance with smaller training datasets. This gain in performance suggests a tantalizing application for rare diseases or other clinical applications where the number of training samples may be quite small. In addition, the features extracted from the Ising-type algorithms have biological relevance. Inspired by quantum process, Ising-type algorithms demonstrate better classification performance than standard machine learning approaches when training with small amounts of data. This suggests a potential application for rare diseases or other clinical data where the amount of training data may be limited. Over the past 2 decades, numerous studies have shown the utility of statistical machine-learning (ML) strategies to classify human malignancies, hypothesize unknown clinical subtypes, and make prognostic predictions based on omics datasets.To address some of these statistical computing limitations, we present a class of unconventional “Ising-type” ML algorithms, inspired by quantum computing. As a rapidly emerging technology, quantum computing promises to enhance the performance of certain classes of statistical computing and ML tasks, such as classification, regression, generation, and resampling. In this nascent discipline, proposals for several quantum ML algorithms have been developed, including quantum principal-component analysis (PCA)Using high-dimensional multi-omics human cancer data from The Cancer Genome Atlas (TCGA), we framed a classification problem in such a way that it was amenable to solving with Ising-type approaches. The Ising-type methods must be formulated as a quadratic unconstrained binary optimization (QUBO) or, equivalently, an Ising Hamiltonian ,,,,We assessed the performance of annealing-based Ising ML algorithms on several TCGA datasets to identify comparative advantages for the Ising approaches. In this ML survey, we compared the performance of Ising models to that of the following commonly used ML algorithms: least absolute shrinkage and selection operator (LASSO),Quantum annealing was implemented on D-Wave physical quantum processors see . As mentLast, we compared the performance of the annealing-based models to a restricted Boltzmann machine (RBM), which is also based on an Ising model. Note, however, that the annealing-based Ising models described above are purely supervised learning approaches, i.e., formulated explicitly with a response variable to be predicted . Boltzmann machines generally seek to explicitly model a data distribution over the inputs as a Boltzmann distribution by incrementally adjusting the In this section, we present classification results for five binomial TCGA cancer dataset comparisons: kidney renal clear cell carcinoma (KIRC) versus kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD) versus lung squamous cell carcinoma (LUSC), breast invasive carcinoma (BRCA) versus matched normal breast tissue , estrogen receptor positive (ERpos) versus estrogen receptor negative (ERneg) breast cancers, and luminal A (LumA) versus luminal B (LumB) breast cancers. We also present findings relative to a six-cancer multiclass classification strategy for human brain, breast, kidney, lung, liver, and colorectal cancer types (see −24). Overall, Field was one of the poorest performing methods relative to the four metrics assessed; however, it performed relatively well on the LumA versus LumB dataset (0.74 ± 0.006). The quantum and classical Ising-type classification results indicate the utility of framing an overall classification strategy as an Ising problem.Although the Ising-type algorithms generally underperformed versus the standard ML methods assessed for these comparisons, the Ising-type classifiers performed well on the LumA versus LumB comparison. Moreover, as with all the standard ML methods used in this work, the most informative feature for classification predicted by the annealing-based Ising models was the first PC, indicating that the Ising models also assigned the greatest weight to the features that account for the most variation within the data. This is consistent with previous results where D-Wave was able to extract a motif for protein-DNA binding that agreed with classical results.Finally, to determine the utility of Ising-type methods on a larger, multiclass classification experimental design, we evaluated classification performance of the standard and Ising-type ML algorithms on a six-cancer, multiclass TCGA dataset. The six TCGA cancer types included brain, breast, kidney, lung, liver, and colorectal cancers see . We, the,,Based on previous work indicating that quantum and classical Ising-type approaches are superior to standard ML classifiers on small training set sizes,−3), as did the other Ising-type methods. Classification performance for all standard ML methods  steadily decreased after a reduction to 50% of the original training data (125 breast tumor samples), whereas we found significantly less reduction in mean balanced accuracies for the five quantum and classical Ising models across the entire training set size spectrum. Furthermore, the Ising models showed a relatively minimal reduction in performance at 95% versus 20% of the original training data  compared with LASSO . Moreover, all five standard ML methods were associated with a significantly higher degree of overfitting than the Ising model classification approaches, an issue that has also historically plagued the analysis of genomic data. −14).At 25%–40% of the original training data (63 to 100 breast tumor samples), the mean balanced accuracies of the five Ising models  were statistically superior to the mean balanced accuracies of the five standard ML algorithms . For example, at 25% of the initial training data D-Wave statistically outperformed SVM, the top standard ML method . Similar to the LumA versus LumB comparison, −17).To assess the generality of this finding that Ising-type methods may perform better than standard ML approaches with a small amount of training data, we performed the same analysis on the ERpos versus ERneg breast cancer and the six-cancer, multiclass datasets. As both datasets were significantly larger than the LumA versus LumB comparison, we reduced each to a much smaller percentage of the initial training set size. −16) on this multiclass cancer dataset. In addition, SA also exhibited a significant performance drop relative to D-Wave at the low end of the training data fraction, although this feature is temperature dependent: by modifying SA's final temperature it can be made to perform as well as D-Wave. This is concordant with previous binomial quantum ML studies.,Analysis of the six-cancer, multiclass dataset further confirmed the ERpos versus ERneg findings. While ,In summary, all methods (with the exception of NB) converged to roughly the same balanced accuracy at a high training data fraction, but at a low fraction all Ising models performed better on three distinct datasets. These findings go beyond previous work−31). We also found that Random (0.81 ± 0.005), SA (0.80 ± 0.005), and D-Wave (0.80 ± 0.006) slightly outperformed three of the five standard ML approaches: SVM (0.79 ± 0.005), NB (0.79 ± 0.006), and LASSO (0.77 ± 0.005). To confirm the multi-omics PCA-derived gene-level classification findings, we performed a simple dual-dimensionality reduction and differential analysis approach on the LumA versus LumB comparison with edgeR.To assess the performance of the Ising-type methods at the gene level, we used the 44 most informative genes, by PCA loading of the first PC (PC1), from the original training set described in the previous sections for the LumA versus LumB breast cancer dataset. Results are presented in RACGAP1 was the most informative feature, as averaged across 9 of the 10 classifiers  of the top 41 mRNA genes. RACGAP1 was ranked only 22 of 44 by PC1 loading. These findings indicate the importance of combined dimensionality reduction/feature learning and classification of high-throughput biological data. From a biological perspective, RACGAP1 is a putative oncogene, which promotes the growth of triple-negative/basal-like breast cancers. Experimental depletion of this gene inhibits cancer cell proliferation by the combined effects of cytokinesis failure, CDKN1A/p21-mediated RB1 inhibition, and the onset of senescence.RACGAP1 in LumB tumors, the more aggressive breast cancer subtype, our gene-level classification results also support our previous findings indicating that Ising-type models are capable of robustly assigning the greatest weight to the most biologically relevant information in a given model. Close inspection of the top 44 genes from PC1 used as molecular features for the LumA versus LumB comparison indicated that cancer and breast cancer  and retinoblastoma-associated protein 1 (E2F1) were implicated in the greatest number of published breast cancer papers  among all of the 44 genes queried (see E2F1 yielded higher PC1 loading (4 versus 15) and averaged information (8.6 versus 33) rankings than HGF. E2F1 is a well-studied transcription factor involved in cell proliferation, differentiation, and apoptosis. It is a member of the E2F protein family, which has been implicated in cell-cycle control and regulation of tumor suppressor proteins. Low E2F1 gene expression is predictive of metastasis-free survival in breast cancer patients.RACGAP1 finding, we determined significantly higher differential mRNA expression of EF2F1 in LumB versus LumA breast cancers via edgeR analysis . Taken together, our gene-level classification results support known breast cancer etiology.We found that all but t cancer D. Of theison see . At the ried see . E2F1 yiWe have presented the first successful demonstration of annealing-based Ising models applied to integrated genome-wide multi-omics human cancer data. We have shown that classification with Ising models is comparable to standard ML strategies on multiple partitions of data of multiple large human cancer datasets. However, it is important to note that the benefit of using quantum annealing cannot be attributed solely to inherent quantum behavior, as SA and our Random control classifier performed similar to, if not better than, quantum annealing as implemented by a D-Wave device on two of the three fractional training dataset comparisons. By randomly generating bit strings and sorting them by their Ising energy, we achieved classification accuracies nearly equal to standard ML and, in some cases, better than both quantum and SA. The comparable performances of our random control strategy and D-Wave and SA are due to a distinction between the objective function for the Ising-type approaches, which is an approximation for the negative log likelihood, and the performance metrics presented . While we describe this discrepancy in more detail in The relative advantage of annealing-based Ising approaches over standard ML approaches when trained with relatively small amounts of data may be attributed to the discrete weights returned for the Ising-type methods. On one hand, discrete weights rendered with Ising-type methods control for statistical shrinkage better than statistical optimization parameters of standard ML approaches. This generalizability issue has plagued the ML field for decades. On the other hand, binary weights limit the informativeness of the standard classifiers; with larger amounts of training data, the Ising-type methods slightly underperformed standard ML approaches. These findings point to the potential application of a new class of algorithms, as simple heuristic models with discrete weights may perform better in situations of limited training data, which is often the case in clinical trials and drug efficacy studies. The relative advantageous trend of enhanced classification performance for Ising-type methods on small amounts of training data is true even when using gene-level features; Although RBMs are formulated quite different from the annealing-based Ising models, in that RBMs are unsupervised and iteratively update model parameters, whereas the annealing-based approaches explored here are supervised with fixed RACGAP1, a putative oncogene in breast cancer, to be associated with the highest averaged information ranking. This finding was supported via independent differential gene expression analysis, indicating that LumB tumors, a more aggressive molecular subtype of breast cancer, were associated with statistically significant, higher mRNA levels of RACGAP1 than LumA tumors. Moreover, our semantic search of full-text records available in the PMC database found that 43 of these top 44 genes have been previously implicated in breast cancer. While our results support previously published findings, it is possible that more sophisticated dimensionality-reduction techniques, such as multi-omics factor analysis,Inherent to all useful biological classifiers, we showed that all the Ising-type algorithms identified relevant molecular features in each cancer comparison. Like the standard ML approaches, these algorithms determined PC1 as the most informative feature for each dataset, from which we then proceeded to perform gene-level classification. Analysis of feature importance of the trained classifiers on the top 44 genes of PC1 for the LumA versus LumB comparison determined While we achieved comparable classification performance on all binomial comparisons assessed in this study, it is important to note that our Ising-type approaches did not perform as well as standard ML on a large multiclass, six-cancer dataset. This observation is most likely related to the relatively larger training dataset used for this multiclass comparison, as the six-cancer dataset comprised approximately 12 times the amount of data relative to the LumA versus LumB dataset. As we showed by reducing the amount of training data for the LumA versus LumB, the ERpos versus ERneg, and the six-cancer multiclass comparisons, the Ising-type approaches performed well with relatively smaller amounts of data but did not statistically improve with incremental increases. Another explanation for the decreased performance of the Ising-type approaches may be related to the fact that the number of approximations used to formulate the classification problem as an Ising Hamiltonian depends on the number of classes see . The appAlthough practical quantum computing architectures are still in development, the demonstration of classification performances of Ising-type approaches that are comparable to standard ML methods on high-dimensional, multi-omics human cancer datasets is encouraging. Our survey of ML classifiers has uncovered a class of algorithms that perform better than standard methods on limited biomedical data: Ising-type methods with discrete weights. This advantage for small experimental designs is particularly useful in medicine, where large datasets may be prohibitively expensive to obtain, or in the study of rare diseases. As technology improves and new algorithms are introduced, we are cautiously optimistic that these unconventional classification algorithms will afford unique insights and drive the discovery of novel approaches for solving complex biological problems.tom.chittenden@genuitysci.com).Tom Chittenden may be contacted for additional information  or cBioportal .,Genomic data from TCGA were retrieved, preprocessed, and normalized. An overview of our data pipeline is depicted in Z score) based on the training data. The same training mean and standard deviation was then applied to the corresponding test data. Furthermore, given that the data comprised more than 79,000 molecular features, dimensionality reduction was conducted in order to make comparisons with existing quantum hardware. As such, we performed PCA on each random, balanced partition of the training data, retaining the top 44 PCs for the binomial datasets and 13 PCs for the six-cancer dataset. The test data were then projected onto the PCs defined by the corresponding training data. The number of PCs was chosen based on the largest number of features that could be accommodated on existing quantum annealing hardware (see the section below on formulating the classification problem as an Ising model). Hyperparameters were selected using cross-validation on the training data  and test/validation (20%) data. Each feature was standardized to zero mean and unit variance  Hamiltonian for this system can be written as:z matrices. Given a spin configuration x matrix acting on qubit We explored the use of quantum annealing with processors made by D-Wave Systems, Inc.raph see . For theitten as:HP=∑i∈VhiFor the results in the main text, we set the annealing time at 5 Similar to quantum annealing, SA accepts problems formulated as an Ising problem, as defined in As another approach to explore the usefulness of the formulating the classification task as an Ising problem, and to check the role played by the couplings  and a hidden layer. Introducing a hidden layer allows one to model more complicated probability distributions. An RBM defines a probability distribution through an energy function as To use RBMs for classification, we used the approach described in Larochelle et al.,We show how to arrive at a simple Ising formulation to model a multiclass classification problem with The goal of training is to find the weights that maximize the probability given the classes in the dataset or, equivalently, to minimize the negative log likelihood. Once the weights are found, inference is straightforward; probabilities for each class are generated and we assign the predicted label based on the class with the highest predicted probability. We can express the negative log likelihood as follows:Taking a second-order Taylor approximation around the argument of the exponential equal to 0, we eventually arrive at the following expression for the negative log likelihood (see embedding, by which edges in the graph are contracted to give a graph with fewer vertices but a higher degree, is generated (see In general, this formulation requires arbitrary interweight couplings (i.e., vity see , an embe