Novel therapies for multiple myeloma (MM) have improved patient survival, but their high costs strain healthcare budgets. End-of-life phases of treatment are generally the most expensive, however, these high costs may be less justifiable in the context of a less pronounced clinical benefit. To manage drug expenses effectively, detailed information on end-of-life drug administration and costs are crucial. In this retrospective study, we analysed treatment sequences and drug costs from 96 MM patients in the Netherlands who died between January 2017 and July 2019. Patients received up to 16 lines of therapy (median overall survival: 56.5 months), with average lifetime costs of (sic)209 871 ((sic)3111/month; range: (sic)3942-(sic)776 185) for anti-MM drugs. About 85% of patients received anti-MM treatment in the last 3 months before death, incurring costs of (sic)20 761 (range: (sic)70-(sic)50 122; 10% of total). Half of the patients received anti-MM treatment in the last 14 days, mainly fully oral regimens (66%). End-of-life treatment costs are substantial despite limited survival benefits. The use of expensive treatment options is expected to increase costs further. These data serve as a reference point for future cost studies, and further research is needed to identify factors predicting the efficacy and clinical benefit of continuing end-of-life therapy.

Background Frailty in newly-diagnosed multiple myeloma (NDMM) patients is associated with treatment-related toxicity, which negatively affects health-related quality of life (HRQoL). Currently, data on changes in HRQoL of frail and intermediate-fit MM patients during active treatment and post-treatment follow-up are absent. Methods The HOVON123 study (NTR4244) was a phase II trial in which NDMM patients ≥75 years were treated with nine dose-adjusted cycles of Melphalan-Prednisone-Bortezomib (MPV). Two HRQoL instruments (EORTC QLQ-C30 and -MY20) were obtained before start of treatment, after 3 and 9 months of treatment and 6 and 12 months after treatment for patients who did not yet start second-line treatment. HRQoL changes and/or differences in frail and intermediate-fit patients (IMWG frailty score) were reported only when both statistically significant (p<0.005) and clinically relevant (>MID). Results 137 frail and 71 intermediate-fit patients were included in the analysis. Compliance was high and comparable in both groups. At baseline, frail patients reported lower global health status, lower physical functioning scores and more fatigue and pain compared to intermediate-fit patients. Both groups improved in global health status and future perspective; polyneuropathy complaints worsened over time. Frail patients improved over time in physical functioning, fatigue and pain. Improvement in global health status occurred earlier than in intermediate-fit patients. Conclusion HRQoL improved during anti-myeloma treatment in both intermediate-fit and frail MM patients. In frail patients, improvement occurred faster and, in more domains, which was retained during follow-up. This implies that physicians should not withhold safe and effective therapies from frail patients in fear of HRQoL deterioration.

Introduction: The development of treatment options for multiple myeloma (MM) has improved survival, but also come with increased treatment costs, which can pose a burden on health care funding and limit access to drugs. Therefore, costs and cost-effectiveness analyses are increasingly important in medical decision making. Such analyses are often based on average health outcomes and costs over a lifetime perspective. However, specifically the end of life (EOL) phase is accompanied by high health care costs, while the contribution of active drug treatment to survival time and quality of life might be minimal. Therefore, we investigated the drug costs from diagnosis to death in detail, in a real-world cohort of MM patients. Methods: We analysed health care records from all MM patients who received (a part of) their treatment in Amsterdam University Medical Centre, and who died between January 1st 2017 and July 1st 2019. We extracted all anti-MM treatments from diagnosis to death, including dose adjustments and start- and stop dates. We calculated drug costs using the Dutch Z-index (indicating drug costs), of August 2020. Results: 96 patients were eligible for analysis and received a median of 5 (range: 1-16) lines of therapy; 61 (63.5%) received a stem cell transplantation (SCT). Time to next treatment or death from 1st to 2nd line of therapy was longer in patients who received an SCT (median 23.9 vs 13.0 months, p=0.002) and was progressively shorter at later lines of therapy, without significant differences between patients who did or did not receive an SCT. Mean total drug costs of MM treatments (from diagnosis to death) were €211,563 (range: €3,942 - €776,185; €3,139 per month [range: €54 – €8,309]). Eighty-two patients (85.4%) received anti-MM treatment in the last 3 months before death and the mean drug costs in this period were €20,361 (range: €70 - €50,466; 9.6% of total). Forty-nine (51.0%) patients received anti-MM treatment in the last 14 days before death and 33 (34.4%) in the last seven days. Mean drug costs per month were approximately twice as high as compared to costs per month over the myeloma life time. (approximately €6,700 versus €3,139 per month), Table 1. Conclusion: The majority of patients received anti-MM therapy during the 14 days preceding death. Associated drug costs were considerable, especially in light of limited survival benefit. Moreover, this is expected to increase as the novel drugs that are currently available also been given at later lines are more expensive. In view of the increasing budget impact of anti-myeloma treatment, hampering access to novel drugs, and the possible negative impact of EOL treatment on QoL, the identification of factors predicting efficacy and clinical benefit of continuing EOL therapy, warrant further investigation. Table 1 - Numbers of patients and (relative) costs of treatment per period. Period Patients receiving treatment (% of total) Mean costs of treatment (range; % of total costs) Mean relative costs per month (range)* Diagnosis to death 96 (100) €211,563 (€3,942 - €776,185; 100) €3,139** (€54 – €8,309) - With SCT - 61 (63.5) - €210,863 (€17,317 - €621,756) - €2,921** (€87 - €8,309) - Without SCT - 35 (36.5) - €212,784 (€3,942 - €776,185) - €3,607** (€54 - €8,072) Last 3 months 82 (85.4) €20,361 (€70 - €50,466; 9.6) €6,787 (€23 - €16,822) Last 30 days 66 (68.8) €6,188 (€10 - €17,444; 2.9) €6,271 (€10 - €17,677) Last 14 days 49 (51.0) €3,001 (€9 - €9,512; 1.4) €6,516 (€20 - €20,655) Last 7 days 33 (34.4) €1,662 (€3 - €4,757; 0.8) €7,218 (€13 - €20,659) (*) 30.4 days(**) mean total costs divided by mean OS per groupAbbreviation: SCT = Stem cell transplantation

provide valuable insight on the potential impact of poverty on ALL outcomes and support the comprehensive, longitudinal collection and analysis of social determinants of health data in childhood cancer clinical trials in the context of well-established prognostic indicators, with the hope of informing future intervention strategies.

Although expanded access is an increasingly used pathway for patients to access investigational medicine, little is known on the magnitude and content of published scientific research collected via expanded access. We performed a review of all peer-reviewed expanded access publications between January 1, 2000 and January 1, 2022. We analyzed the publications for drugs, diseases, disease area, patient numbers, time, geographical location, subject, and research methodology (single center/multicenter, international/national, prospective/retrospective). We additionally analyzed endpoints reported in all COVID-19-related expanded access publications. We screened 3810 articles and included 1231, describing 523 drugs for 354 diseases for 507,481 patients. The number of publications significantly increased over time ( p<0.001 ). Large geographical disparities existed as Europe and the Americas accounted for 87.4

Background: Frailty in non-transplant eligible (NTE) newly diagnosed multiple myeloma (NDMM) patients is associated with toxicity which can negatively affect physical functioning and quality of life (QoL). Older patients may prefer QoL and physical independence over length of life, highlighting the importance of taking health-related (HR) QoL assessment into account for treatment guidance. Methods: The HOVON123 study (NTR4244) was a phase II trial in which 238 NTE-NDMM patients ≥75 years were treated with 9 dose-adjusted cycles MPV. Nine (3 functional; 6 symptom) subscales of two HRQoL instruments (EORTC QLQ-C30 and MY20) were obtained at baseline (T0), after 3 (T1) and 9 (T2) cycles of therapy, and 6 (T3) and 12 (T4) months after discontinuation of therapy in patients without progression. The presence of “tingling hands/feet” was used as a proxy for neuropathy. Differences in baseline HRQoL were analysed with independent t-tests and changes over time with linear mixed models. HRQoL changes and/or differences were reported only when both statistically significant (p<0.005, adjusted for multiple testing) and clinically relevant (>MID). Results: A total of 137 frail and 71 intermediate-fit patients were included in the HRQoL analysis, after exclusion of fit patients and patients whose frailty status or baseline HRQoL questionnaire was missing. Compliance was not materially different in both groups. Frail patients had an inferior HRQoL at baseline in the subscales global health status, physical functioning, fatigue and pain, compared with intermediate-fit patients. Both groups reported improvements in global health status and future perspective. In contrast to intermediate fit patients, frail patients improved in physical functioning, fatigue and pain over time. The improvements in global health status were reached earlier in frail patients (T1) compared with intermediate fit patients (T2), Figure 1. In both intermediate fit and frail patients there was an increase in neuropathy. All other subscales remained within MID ranges and/or were not statistically significant different from baseline. The improvement in global health status sustained after treatment completion (T3-T4) both for frail and intermediate fit patients. This also accounted for future perspective at T3, however, at T4 for intermediate fit patients only. In contrast, the improvement in all other HRQoL domains during treatment, lost clinical relevance and/or statistical significant difference during the TFI. The deterioration in neuropathy remained until T4 in frail patients, but not for intermediate fit patients, reversing at T4, Figure 1. Conclusion: HRQoL in frail patients is inferior as compared to intermediate fit patients at diagnosis. Importantly, treatment improved HRQoL, irrespective of frailty level, being more pronounced and occurring even faster in frail patients. Therefore, physicians should not withhold therapy in these patients because of their frailty status only.

Patients with rare diseases often have limited or no options for approved treatments or participation in clinical trials. In such cases, expanded access (or “compassionate use”) provides a potential means of accessing unapproved investigational medicines. It is also possible to capture and analyze clinical data from such use, but doing so is controversial. In this perspective, we offer examples of evidence derived from expanded access programs for rare diseases to illustrate its potential value to the decision-making of regulators and payers in the European Union and the United States. We discuss ethical and regulatory aspects to the use of expanded access data, with a focus on rare disease medicines. The heterogeneous approach to expanded access among countries within the European Union leaves uncertainties to what extent data can be collected and analyzed. We recommend the issuance of new guidance on data collection during expanded access, harmonization of European pathways, and an update of existing European compassionate use guidance. We hereby aim to clarify the supportive role of expanded access in evidence generation. Harmonization across Europe of expanded access regulations could reduce manufacturer burdens, improve patient access, and yield better data. These changes would better balance the need to generate quality evidence with the desire for pre-approval access to investigational medicine.

ObjectivesTo evaluate the incremental value of new drugs across disease areas receiving favourable coverage decisions by the UK’s National Institute for Health and Care Excellence (NICE) over the past decade.Design, setting, and participantsThis cross-sectional study assessed favourable appraisal decisions of drugs between 1 January 2010 and 31 December 2020. Estimates of incremental benefit were extracted from NICE’s evidence review groups reports.Primary outcome measureIncremental benefit of novel drugs relative to the best alternative therapeutic option, expressed in quality-adjusted life-years (QALYs).Results184 appraisals of 129 drugs provided QALYs. The median incremental value was 0.27 QALY (IQR: 0.07–0.73). Benefits varied across drug-indication pairs (range: −0.49 to 5.22 QALY). The highest median benefits were found in haematology (0.70, IQR: 0.55–1.22) and oncology (0.46, IQR: 0.20–0.88), the lowest in ophthalmology (0.09, IQR: 0.04–0.22) and endocrinology (0.02, IQR: 0.01–0.06). Eight appraisals (4.3%) found contributions of more than two QALYs, but one in four (50/184) drug-indication pairs provided less than the equivalent of 1 month in perfect health compared to existing treatments.ConclusionsIn our review period, the median incremental value of novel drugs approved for use within the English National Health System, relative to the best alternative therapeutic option, was equivalent to 3–4 months of life in perfect health, but data were heterogeneous. Objective evaluations of therapeutic value helps patients and physicians to develop reasonable expectations of drugs and delivers insights into disease areas where medicinal therapeutic progress has had the most and least impact.

Objectives To quantify and characterise the usage of expanded access (EA) data in National Institute for Health and Care Excellence (NICE) technology appraisals (TAs). EA offers patients who are ineligible for clinical trials or registered treatment options, access to investigational therapies. Although EA programmes are increasingly used to collect real-world data, it is unknown if and how these date are used in NICE health technology assessments. Design Cross-sectional study of NICE appraisals (2010–2020). We automatically downloaded and screened all available appraisal documentation on NICE website (over 8500 documents), searching for EA-related terms. Two reviewers independently labelled the EA usage by disease area, and whether it was used to inform safety, efficacy and/or resource use. We qualitatively describe the five appraisals with the most occurrences of EA-related terms. Primary outcome measure Number of TAs that used EA data to inform safety, efficacy and/or resource use analyses. Results In 54.2% (206/380 appraisals), at least one reference to EA was made. 21.1% (80/380) of the TAs used EA data to inform safety (n=43), efficacy (n=47) and/or resource use (n=52). The number of TAs that use EA data remained stable over time, and the extent of EA data utilisation varied by disease area (p=0.001). Conclusion NICE uses EA data in over one in five appraisals. In synthesis with evidence from well-controlled trials, data collected from EA programmes may meaningfully inform cost-effectiveness modelling.

The American Society of Hematology (ASH) annual meeting is often considered the premier international conference in hematology. When "positive" results from single-arm phase 2 trials are presented at ASH, one would expect that these herald therapeutic advances, or at a minimum, follow into a larger, potentially confirmatory, randomized (phase 3) trial. However, a survey of ASH abstracts reporting positive results in acute myeloid leukemia (AML) indicated that these positive abstracts, accounting for the majority of abstracts, had limited predictive value for subsequent clinical utility.1 As phase 3 trials are initiated to further investigate clinical benefit, proceeding (or not) from a positive abstract to a phase 3 study is a topic that warrants investigation. Across all hematological malignancies, it is currently unclear how often positive (or negative) abstracts lead to phase 3 trials, as are the reasons why or why not. Here, we hypothesized that most ASH abstracts reporting on phase 2 trials in oncological hematology are positive, and that the majority of drugs in these trials will not reach investigation in phase 3. Finally, because abstracts are necessarily preliminary, subsequent peer-reviewed publication is essential for correct dissemination of trial results.

Background: Novel therapies for multiple myeloma (MM) promise to improve outcomes but are also associated with substantial increasing costs. Evidence regarding cost-effectiveness of novel treatments is necessary, but a comprehensive up-to-date overview of the cost-effectiveness evidence of novel treatments is currently lacking. Methods: We searched Embase, Medline via Ovid, Web of Science and EconLIT ProQuest to identify all cost-effectiveness evaluations of novel pharmacological treatment of MM reporting cost per quality-adjusted life year (QALY) and cost per life year (LY) gained since 2005. Quality and completeness of reporting was assessed using the Consolidated Health Economic Evaluation Reporting Standards. Results: We identified 13 economic evaluations, comprising 32 comparisons. Our results show that novel agents generate additional LYs (range: 0.311–3.85) and QALYs (range: 0.1–2.85) compared to backbone regimens and 0.02 to 1.10 LYs and 0.01 to 0.91 QALYs for comparisons between regimens containing two novel agents. Lifetime healthcare costs ranged from USD 60,413 to 1,434,937 per patient. The cost-effectiveness ratios per QALY gained ranged from dominating to USD 1,369,062 for novel agents compared with backbone therapies and from dominating to USD 618,018 for comparisons between novel agents. Conclusions: Cost-effectiveness ratios of novel agents were generally above current willingness-to-pay thresholds. To ensure access, cost-effectiveness should be improved or cost-effectiveness ratios above current thresholds should be accepted.

David G. J. Cucchi, Christian M. Vonk, Melissa Rijken, François G. Kavelaars, Pauline A. Merle, Elvira Verhoef, Bianca Venniker-Punt, Zinia J. Kwidama, Patrycja Gradowska, Bob L€ owenberg, Jeroen J. W. M. Janssen, Jacqueline Cloos, and Peter J. M. Valk Department of Hematology, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Department of Hematology, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands

Precision medicine is gaining importance in the treatment of acute myeloid leukemia (AML). Objectively reviewing past and current knowledge aids guiding future research. Therefore, we provide a complete overview of all phase II and phase III trials investigating targeted therapies in AML and their primary endpoints over the past two decades in perspective of their clinical benefit. We assessed whether drugs were primarily designed to treat AML or were repurposed and how successful they were based on progression of distinct drugs from phase II to phase III to FDA-approval. Between January 2000 and September 2020, 167 agents with 96 targets were investigated in 397 phase II trials. Twenty-eight agents were steered towards phase III, after three phase II trials on average. Repurposed drugs less often advanced in clinical development than drugs primarily developed for AML. Composite responses were the most prevalent primary endpoints in phase II. Of the eight FDA-approved drugs, none investigated quality of life at time of approval, and three out of eight have yet to show benefit in overall survival. Returns on targeted therapy research remain lean for AML patients. Future trials should not overlook non-targeted agents and foremost study endpoints proven to predict patient well-being.

Expanded access is a pathway to access unregistered medicines if there are no registered treatments available and patients cannot enroll in clinical trials. Expanded access may serve as a last resort for patients who are in dire need of treatment options and cannot await the completion of drug development and for patients who may benefit from treatments that are not (or not anymore) registered in their jurisdiction. Unregistered medicine can be acquired via named-patient pathways ('Leveren op Artsenverklaring') or via group programs ('Compassionate Use Programma's). We describe the origins of expanded access and its daily practice in the Netherlands. We observe an increasing trend in expanded access requests. The potential risks these treatments provide, the possibility of ceasing further treatment and the preferences of individual patients should all inform the decision whether or not to pursue expanded access.

Kinase hyperactivity is a common driver of acute myeloid leukemia (AML) and serves as a therapeutic target.1 The most frequent activating genetic aberrations in AML are internal tandem duplications (~23%) and tyrosine kinase domain mutations (~7%) of FMS-like tyrosine kinase 3 (FLT3-ITD and FLT3-TKD), and the presence of FLT3-ITD negatively affects survival.2 Combined with chemotherapy, FLT3-Tyrosine Kinase Inhibitor (FLT3-TKI) midostaurin improves overall survival in newly diagnosed FLT3-mutated AML, whereas the single agent gilteritinib proved superior to chemotherapy in relapsed/refractory FLT3-mutated AML.2 The presence of FLT3-ITD is predictive for response to FLT3-TKIs,3 yet 41%–56% of FLT3-WT patients respond to FLT3-TKIs, indicating alternative possibilities of FLT3 pathway activation or TKI off-target effects leading to unexpected treatment response.4 Others have identified genomic and global phosphorylation markers associated with FLT3-TKI response in FLT3-WT AML.5,6 As the primary targets of currently approved FLT3-TKIs are tyrosine (Y) kinases, we hypothesized that the direct evaluation of tyrosine kinome could reveal phosphorylation markers associated with FLT3-TKI response. Therefore, we performed both label-free pY-based and global phosphoproteomics7 in 35 primary AML samples (18 FLT3-WT, 17 FLT3-ITD, details provided in Supplemental Digital Table 1, https://links.lww.com/HS/A167) to identify differential phosphorylation underlying response to the FLT3-TKIs gilteritinib and midostaurin. We identified a total of 3.024 unique phosphosites (median 1.666 per sample, range 1.091–2.118; Supplemental Digital Figure 1, https://links.lww.com/HS/A167 and Supplemental Digital Table 2A, https://links.lww.com/HS/A167) in the pY and 27.821 unique phosphosites in the global phosphoproteome dataset. Two samples were excluded due to the low number (382 and 550) of identified phosphosites. Details are provided in the Supplemental Digital Materials and Methods, https://links.lww.com/HS/A167 and Supplemental Digital Table 2B, https://links.lww.com/HS/A167. We then assessed ex vivo response toward FLT3-TKIs by liquid culture and cell viability testing of AML blasts using flow cytometry (Supplemental Digital Figure 2, https://links.lww.com/HS/A167). Of 33, 19 AMLs yielded interpretable dose-response curves and were included in further analyses. As expected, FLT3-ITD samples were more responsive toward gilteritinib and midostaurin, compared with FLT3-WT samples (Figure 1A and B).3 We observed the most pronounced response of FLT3-ITD samples toward gilteritinib, exemplifying the known more potent and specific inhibition of FLT3 by gilteritinib compared with midostaurin, which has a broad inhibition profile (EC50 12.9 versus 635.03 nM, https://proteomicsdb.org, Figure 1C).Figure 1.: Response toward FLT3-TKIs and differential phosphorylation profiles and kinase activity scores associated with FLT3-TKI response. Ex vivo response of FLT3-WT and FLT3-ITD AML blasts toward (A) gilteritinib and (B) midostaurin. P values are calculated using least squares fit regression comparing FLT3-WT and FLT3-ITD samples. As workflow control, response of MV4:11, a homozygous FLT3-ITD AML cell line, is shown. (C) Protein target space of gilteritinib and midostaurin. The top 12 targets are shown, ranked on EC50, which is the drug concentration at which half of the target is competed. Data are retrieved from https://proteomicsDB.org. Individual LC50 values as determined in liquid culture towards (D) gilteritinib and (E) midostaurin. The P value is determined using Wilcoxon rank-sum test. Additional samples for which no phosphoproteomics data was available are shown to illustrate the diversity in FLT3-TKI response. Phosphotyrosine phosphorylation profiles of significant (P < 0.05) differentially phosphorylated phosphosites between responsive and resistant primary AML samples towards (F) gilteritinib and (G) midostaurin. For heatmaps, Euclidean distance with complete linkage for rows and columns was applied. (H) INKA scores based on the pY analyses between gilteritinib responsive and resistant samples, based on median LC50. (I) INKA score of MAPK1 based on the global phosphorylation analyses. P values are calculated by Wilcoxon rank-sum tests. (J) ELISA validation of pERK intensity as determined by pY phosphoproteomics, annotated with gilteritinib response. Correlation coefficient and P value were calculated using Pearson correlation. AML = acute myeloid leukemia; INKA = integrative inferred kinase activity; pY = phosphotyrosine.Responses toward gilteritinib and midostaurin could not be fully explained by the presence of FLT3-ITD, with responses observed in FLT3-WT samples and relative resistance—exemplified by relatively high LC50 values—in FLT3-ITD samples (Figure 1D and E). To explore associations between response and phosphorylation, we compared phosphoproteomic profiles independent of FLT3-ITD status. We defined responsive and resistant samples based on the variation in LC50 values between patients: median LC50 for gilteritinib and the lowest and highest 25th percentile for midostaurin. For the pY phosphoproteome, the FLT3-ITD-independent response toward gilteritinib was associated with differential phosphorylation of 28 phosphosites (P < 0.05, Figure 1F and Supplemental Digital Table 3A, https://links.lww.com/HS/A167). Phosphosites with higher phosphorylation in gilteritinib-resistant samples included MAPK1-Y185, MAPK1-T187 (ERK2) and MAPK3-Y202 and MAPK3-T204 (ERK1), in concordance with other data on FLT3-TKI resistance, but not of FLT3 itself.5,8–11 Posttranslational Modification Signature Enrichment Analysis (PTM-SEA, https://github.com/broadinstitute/ssGSEA2.0) indicated enrichment of EGFR1 (P < 0.05) and KIT (P < 0.2) pathway-associated phosphosites in resistant samples (Supplemental Digital Figure 3A, https://links.lww.com/HS/A167). Differential phosphorylation related to midostaurin response (Figure 1G and Supplemental Digital Table 3B, https://links.lww.com/HS/A167) was more diverse with 46 significant phosphosites, including high phosphorylation of STAT6-Y531 in midostaurin responsive samples. Gene ontology analyses (g:Profiler, https://biit.cs.ut.ee/gprofiler/gost) revealed general processes related to kinase binding and transmembrane signaling. Ras-Raf-MEK-ERK-related phosphosites were absent among the identified differentially phosphorylated sites, although overexpression of RGL4—a regulator of this cascade—has been related to midostaurin response.5 Comparison of global phosphoproteomic profiles between gilteritinib responsive and resistant samples did not yield any clear response-specific phosphorylation profiles (Supplemental Digital Figure 4A, https://links.lww.com/HS/A167). However, PTM-SEA revealed enrichment of phosphosites associated with KIT pathway activation (P < 0.05) and GSK3B activity (P < 0.1) in gilteritinib-resistant AML samples (Supplemental Digital Figure 3B, https://links.lww.com/HS/A167), which are independent from FLT3-ITD mutation status (Supplemental Digital Figure 3C and D, https://links.lww.com/HS/A167). Integrative inferred kinase activity (INKA12) analysis of the pY phosphoproteome identified high activity of MAPK1, MAPK3, and GSK3A-B in gilteritinib-resistant samples, and confirmed that there was no differential activity of FLT3 (Figure 1H). Similarly, INKA analysis of the global phosphoproteome indicated higher activity of MAPK1 in gilteritinib-resistant samples (Figure 1I and Supplemental Digital Figure 5A–E, https://links.lww.com/HS/A167), suggesting that activation of FLT3-independent pathways may abrogate FLT3-inhibition and alternative pathways for survival may be targetable. Validation of pERK1/2 levels using ELISA suggests that high ERK phosphorylation is indeed associated with impaired response toward gilteritinib (Figure 1J). No significantly different INKA scores were identified in the midostaurin responsive versus resistant comparison. To investigate which differential phosphorylated phosphosites between responsive and resistant samples were truly independent of FLT3-ITD status, we assessed phosphorylation differences between FLT3-WT and FLT3-ITD untreated de novo AML samples. As AML sample heterogeneity could hamper mutation-based analyses, we selected samples enriched with FLT3-ITD-positive blasts, on the basis of an allelic ratio of ≥ 0.5. In the pY data, 61 phosphosites were differentially (P < 0.05) phosphorylated between FLT3-WT and FLT3-ITD AML. Phosphorylation of STAT5A-Y90 and LYN-Y265 was significantly higher in FLT3-ITD AML compared with FLT3-WT AML (Figure 2A–D and Supplemental Digital Table 3C, https://links.lww.com/HS/A167). STAT5A is a known downstream component of FLT3-ITD signaling.13 While LYN may be activated by both FLT3-WT and FLT3-ITD, higher stochiometry of phosphorylation of FLT3-ITD may lead to higher binding of LYN.1,14 Surprisingly, phosphorylation of FLT3 itself seemed independent of the presence of in-sample FLT3-ITD (Figure 2E and Supplemental Digital Figure 6, https://links.lww.com/HS/A167), indicating that not overall activity, but differential downstream activation distinguishes FLT3-WT from FLT3-ITD samples. Six phosphosites overlapped between differentially phosphorylated phosphosites of the mutation- and gilteritinib-response comparison (Figure 2F). Mutation-independent, response-specific differential phosphorylation (n = 22) included higher phosphorylation of MAPK1-T185, VIM-T63, STAT1-Y701, and Src-family kinases YES1, FYN, and SRC in gilteritinib-resistant samples. Phosphorylation of PTPN18-Y319 was low in resistant samples (Figure 2G). Global phosphorylation patterns were not clearly distinct between FLT3-WT and FLT3-ITD (Supplemental Digital Figure 4B and C, https://links.lww.com/HS/A167), although PTM-SEA identified known activation of mTOR and Pi3K-AKT signaling in FLT3-ITD samples (Supplemental Digital Figure 3D, https://links.lww.com/HS/A167).15Figure 2.: Phosphoproteomic characterization of FLT3-WT and FLT3-ITD AML and parallel treatment with FLT3- and MEK/ERK inhibitors of primary AML samples. (A) Volcanoplot of differentially (P < 0.05) phosphorylated proteins between FLT3-WT and FLT3-ITD AML samples and log2 fold changes. Specific phosphorylation according to FLT3-ITD status in AML samples of (B) STAT5-Y90; (C) LYN-Y265;244; (D) SPTLC1-Y82; and (E) FLT3-Y842. P values are calculated by Wilcoxon rank-sum tests. (F) Overlapping and unique differentially phosphorylated phosphosites from the pY phosphoproteomics comparisons of responsive and resistant samples towards gilteritinib and midostaurin, and FLT3-WT versus FLT3-ITD-AR > 0.5. (G) Normalized phosphosite intensities determined using pY-based phosphoproteomics of selected unique phosphosites between gilteritinib resistant and responsive AML samples. (H) Kinase target space of ulixertinib and trametinib. All targets are shown, ranked on EC50, which is the drug concentration at which half of the target is competed. Data from https://proteomicsDB.org. (I, K, L) Combination treatment of AML samples with gilteritinib plus the LC10 of trametinib or ulixertinib and their individual INKA profiles from the pY and global phosphoproteomic analyses. (J) Combination treatment of AML7 with parallel increasing concentrations of gilteritinib and trametinib indicates synergism, exemplified by an overall Bliss synergy score of > 10. AML = acute myeloid leukemia; INKA = integrative inferred kinase activity; pY = phosphotyrosine.To further characterize FLT3-WT and FLT3-ITD AML on the protein expression level, we performed proteomics on 17 AML samples (9 FLT3-WT, 8 FLT3-ITD) with sufficient material for additional analyses. On the proteomic level, 4.092 unique proteins were identified, and 199 proteins were differentially (P < 0.05) expressed between FLT3-WT and FLT3-ITD samples (Supplemental Digital Figure 7A, https://links.lww.com/HS/A167 and Supplemental Digital Table 4, https://links.lww.com/HS/A167). Gene ontology analyses of proteins with a minimal fold change of 2 indicated that these proteins were primarily involved in leukocyte activation, oxidation-reduction processes, and protein activation cascades, including oncogenic MAPK signaling, stressing its important role in FLT3-ITD-biology (Supplemental Digital Figure 6B, https://links.lww.com/HS/A167). An integrated network with differentially expressed proteins and phosphorylated phosphosites for the FLT3-WT versus FLT3-ITD comparison—informed by the proteomic, global, and pY phosphoproteomic analyses—revealed relevant differential biology associated with FLT3-ITD-status, in particular cell activation (light green cluster), regulation of cell cycle (red cluster), and RNA splicing (light blue cluster) (Supplemental Digital Figure 8A, https://links.lww.com/HS/A167). Impaired drug response associated with alternative pathway activation may be overcome by simultaneous blocking of those pathways. To replicate previously observed ex vivo therapeutic benefit of parallel MEK inhibition,9 we assessed whether responses toward FLT3-TKIs would improve when treatment was combined with the MEK-inhibitor trametinib (Figure 2H). We only observed marginal decreases in LC50 for gilteritinib combined with fixed concentrations of trametinib (Figure 2I, K, L and Supplemental Digital Figure 9, https://links.lww.com/HS/A167). Surprisingly, we even observed an increase in LC50 towards both gilteritinib and midostaurin in several AML cases (Supplemental Digital Figures 9 and 10, https://links.lww.com/HS/A167), possibly explained by competitive antagonism or unexpected off-target effects. Using parallel increasing concentrations of gilteritinib or midostaurin and trametinib, synergy (exemplified by overall Bliss scores of > 10 [https://synergyfinder.fimm.fi/]) was only observed in a sample harboring an NRAS mutation (Figure 2J and Supplemental Digital Figures 9 and 10; https://links.lww.com/HS/A167), which may activate MEK-ERK signaling. Additionally, we explored the therapeutic effect of ulixertinib—a novel pan-ERK inhibitor. Combining gilteritinib with ulixertinib more efficiently enhanced response (Figure 2I, K, and L) than with trametinib. Combination of midostaurin with ulixertinib did not enhance responses (Supplemental Digital Figure 10, https://links.lww.com/HS/A167). Responses may be improved by optimizing concentrations and timing to prevent competitive antagonistic effects. Based on INKA ranking, AML patient-specific drug combinations could be explored1 such as inhibition of KIT in AML2393: the differential phosphorylation of 2 KIT sites in the gilteritinib resistant samples (Figure 2F and Supplemental Digital Figure 8B, https://links.lww.com/HS/A167)—in tandem with the enrichment of KIT pathway components (Supplemental Digital Figure 3B, https://links.lww.com/HS/A167)—may in part explain our observations. KIT itself is a known driver of leukemogenesis and not inhibited by gilteritinib. KIT-Y936 site phosphorylation is a docking site for several signal transduction molecules, including GRB2 and CBL.16 Binding of GRB2 to KIT can recruit GAB2 and may thereby mediate alternative activation of the MAPK signaling pathway and additionally activate the PI3K-Akt pathway in the gilteritinib-resistant samples.17 Future studies may therefore explore combination treatment of gilteritinib with a KIT inhibitor in FLT3-TKI-resistant AML. Nevertheless, the marginal benefit of combining trametinib with FLT3-TKIs is discordant with previous reports9 and warrants clarification to maximize the benefit of combinations with potentially toxic MEK inhibitors in clinical studies. Our study has a few limitations. First, sample selection is biased toward highly proliferative AMLs allowing for ample (≥ 4.5 mg) protein extraction for in-depth pY phosphoproteomics analysis. Second, although we selected samples with high blast counts and enriched for mononuclear cells during pre-processing, samples also contained variable numbers of normal leukocytes. The small (< 10%) fraction of normal leukocytes present in the samples may have led to identification of some normal leukocyte biology associated phosphorylation events in the (phospho)proteomics datasets. However, this should not affect the profiles associated with mutation status and drug response. Third, the observed associations are based on the ex vivo response of primary AMLs in liquid culture and require mechanistic validation using conditions mimicking the BM microenvironment,18 which may impact the observed responses. As we compare relative resistance among samples cultured in identical culture conditions, our experiments still provide valuable information regarding response mechanisms. Nevertheless, clinical translation and validation of our findings is warranted: to further clarify response mechanisms on the phosphorylation level, future studies analyzing BM of patients treated with monotherapy FLT3-TKIs are required. Considering the current developments in AML treatment, however, most clinical studies will combine TKIs with other (targeted) agents or chemotherapy,19 which should be taken into account in future research. In conclusion, we present an in-depth clinical phosphoproteome dataset, characterizing FLT3-ITD AML and FLT3-TKI responses. We observed distinct phosphorylation signatures and protein expression profiles associated with response towards gilteritinib and midostaurin. Our ex vivo drug combination studies indicate that further exploration of the role of ERK and simultaneous blocking the MEK-ERK axis is warranted to maximize the potential benefit of treatment combinations aiming to improve responses to FLT3-TKIs. The identification of key proteins and phosphorylation events in FLT3-ITD-AML serve as a reference for future exploration of phosphoproteomic biomarkers associated with FLT3-ITD AML and FLT3-TKI response. Disclosures JJWMJ received research funding from Novartis and Bristol Myers Squibb; speaker fees from Incyte and Pfzer; advisory board honoraria from AbbVie, Incyte, Novartis, and Pfizer. He is the President of the Apps for Care and Science Foundation that develops the HematologyApp and which has received funding from Abbvie, Amgen, Astellas, Celgene/Bristol Myers Squibb, Daiichi-Sankyo, Incyte, Janssen, Jazz, Novartis, Sanofi Genzyme, Takeda, Roche, and Servier. All the other authors have no conflicts of interest to disclose. Sources of funding This study was partly funded by Stichting Egbers and Cancer Center Amsterdam (CCA2014-1-15 and CCA2012-5-08). Cancer Center Amsterdam and Netherlands Organization for Scientific Research (NWO Middelgroot, #91116017) are acknowledged for support of the mass spectrometry infrastructure.

Reports of “positive” results in early phase trials as presented at ASH presumably herald therapeutic advances, or at a minimum, a larger, potentially confirmatory, randomized trial. However, the predictive value of an ASH abstract reporting positive results in AML for subsequent clinical utility seems low (Estey 2006, ASH). Furthermore, not all results presented at ASH are published in peer-reviewed journals, and selectively publishing positive results leads to publication bias. Moreover, truly negative studies may be scientifically more rigorous and accurate than positive studies given the unequivocal findings. The extent of publication bias is unknown as is the frequency with which positive or negative abstracts lead to subsequent investigation in phase III and the reasons why positive phase II studies might not progress to phase III.