Efficacy of immune checkpoint inhibitors in alveolar soft-part sarcoma: results from a retrospective worldwide registry

Alveolar soft-part sarcoma; Immune checkpoint; ImmunotherapySarcoma alveolar de partes blandas; Puntos de control inmunológico; InmunoterapiaSarcoma alveolar de parts toves; Punts de control immunològic; ImmunoteràpiaBackground Conventional cytotoxic drugs are not effective in alveolar soft-part sarcoma (ASPS). Immune checkpoint (programmed cell death protein 1/programmed death-ligand 1) inhibitors (ICIs) are promising drugs in ASPS. A worldwide registry explored the efficacy of ICI in ASPS. Materials and methods Data from adult patients diagnosed with ASPS and treated with ICI for advanced disease in expert sarcoma centers from Europe, Australia and North America were retrospectively collected, including demographics and data related to treatments and outcome. Results Seventy-six ASPS patients, with a median age at diagnosis of 25 years (range 3-61 years), were registered. All patients received ICI for metastatic disease. Immunotherapy regimens consisted of monotherapy in 38 patients (50%) and combination in 38 (50%) (23 with a tyrosine kinase inhibitor). Among the 68 assessable patients, there were 3 complete responses and 34 partial responses, translating into an overall response rate of 54.4%. After a median follow-up of 36 months [95% confidence interval (CI) 32-40 months] since the start of immunotherapy, 45 (59%) patients have progressed on ICI, with a median progression-free survival (PFS) of 16.3 months (95% CI 8-25 months). Receiving ICI in first line (P = 0.042) and achieving an objective response (P = 0.043) correlated with a better PFS. Median estimated overall survival (OS) from ICI initiation has not been reached. The 12-month and 24-month OS rates were 94% and 81%, respectively. Conclusions This registry constitutes the largest available series of ASPS treated with ICI. Our results suggest that the ICI treatment provides long-lasting disease control and prolonged OS in patients with advanced ASPS, an ultra-rare entity with limited active therapeutic options.DSM is a recipient of a Sara Borrell postdoctoral fellowship funded by the National Institute of Health Carlos III (ISCIII) (CD20/00155). The authors thank SELNET project. SELNET has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 825806

Health-related quality of life and pain with selinexor in patients with advanced dedifferentiated liposarcoma

[Objective] Compare health-related quality of life (HRQoL) of selinexor versus placebo in patients with dedifferentiated liposarcoma.[Materials & methods] HRQoL was assessed at baseline and day 1 of each cycle using the European Organization for Research and Treatment of Cancer 30-item core quality of life questionnaire. Results were reported from baseline to day 169 (where exposure to treatment was maximized while maintaining adequate sample size).[Results] Pain scores worsened for placebo versus selinexor across all postbaseline visits, although differences in HRQoL at some visits were not significant. Other domains did not exhibit significant differences between arms; however, scores in both arms deteriorated over time.[Conclusion] Patients treated with selinexor reported lower rates and slower worsening of pain compared with patients who received placebo.This study was funded by Karyopharm Therapeutics, Inc. M Gounder: reports an institutional research grant from Karyopharm, personal fees from Karyopharm, Epizyme, Springworks, Daiichi, Bayer, Amgen, Tracon, Flatiron, Medscape, Physicians Education Resource, Guidepoint, GLG and UpToDate; and grants from the National Cancer Institute, National Institutes of Health (P30CA008748) – core grant (CCSG shared resources and core facility). ARA Razak: consulting/Ad board: Merck & Adaptimmune Research support: Karyopharm Therapeutics, Deciphera, Blueprint Medicines, Pfizer, Adaptimmune, Merck, Roche/Genentech, Bristol-Myers Squibb, Medimmune, Amgen, GSK, AbbVie, Iterion Therapeutics. AM Gilligan: employee of Karyopharm Therapeutics, Inc. H Leong: employee of Karyopharm Therapeutics, Inc. X Ma: employee of Karyopharm Therapeutics, Inc. N Somaiah: consultant for Deciphera, Blueprint, Bayer Research Support from Ascentage, Astra-Zeneca, Daiichi-Sankyo, Deciphera, Eli Lilly, Karyopharm and GSK. SP Chawla: consultant for Amgen, Roche, GlaxoSmithKline, Threshold Pharmaceuticals, CytRx Corporation, Ignyta, Immune Design, TRACON Pharma, Karyopharm Therapeutics, SARC: Sarcoma Alliance for Research though Collaboration, Janssen, Advenchen Laboratories, Bayer, NKMax, InhibRx. Grants or contracts from Amgen, Roche, GlaxoSmithKline, Threshold Pharmaceuticals, CytRx Corporation, Ignyta, Immune Design, TRACON Pharma, Karyopharm Therapeutics, SARC: Sarcoma Alliance for Research though Collaboration, Janssen, Advenchen Laboratories, Bayer, InhibRx, NKMax. G Grignani: consultant for Eli Lilly, Novartis, Glaxo, Pharmamar, EISAI, Bayer, Merck. SM Schuetze: consultant – NanoCarrier, UpToDate. Research funding to institution – Adaptimmune, Amgen, Blueprint, Glaxo-SmithKline, Karyopharm. B Vincenzi: Consultant for Pharmamar Eisai, Lilly, Abbott, Novartis, Accord AJ Wagner: consultant for Daiichi-Sankyo, Deciphera, Eli Lilly, Epizyme, NovoCarrier, Mundipharma, and Research Support to My Institution from Aadi Bioscience, Daiichi-Sankyo, Deciphera, Eli Lilly, Karyopharm and Plexxikon. RL Jones: consultant for Adaptimmune, Athenex, Bayer, Boehringer Ingelheim, Blueprint, Clinigen, Eisai, Epizyme, Daichii, Deciphera, Immunedesign, Lilly, Merck, Pharmamar, Springworks, Tracon, Upto Date. J Shah: employee of Karyopharm Therapeutics, Inc. S Shacham: employee of Karyopharm Therapeutics, Inc. M Kauffman: employee of Karyopharm Therapeutics, Inc. RF Riedel: ownership - Limbguard, LLC (Spouse); Institutional Clinical Research Support - AADi, AROG, Blueprint, Daiichi-Sankyo, Deciphera, Glaxo-SmithKline, Karyopharm, Ignyta, Immune Design, NanoCarrier, Oncternal, Philogen, Plexxikon, Roche, Springworks, Tracon; Consultant/Advisor - Bayer, Blueprint, Daiichi-Sankyo, Deciphera, Ignyta, NanoCarrier. S Attia: reports research funding from Desmoid Tumor Research Foundation and research funding to their institution from: AB Science, TRACON Pharma, Bayer, Novartis, Lilly, Immune Design, Karyopharm Therapeutics, Epizyme, Blueprint Medicines, Genmab, CBA Pharma, Merck, Philogen, Gradalis, Deciphera, Takeda, Incyte, Springworks, Adaptimmune, Advenchen Laboratories, Bavarian Nordic, BTG, PTC Therapeutics, GlaxoSmithKline, FORMA Therapeutics. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Peer reviewe

Autoimmune PaneLs as PrEdictors of Toxicity in Patients TReated with Immune Checkpoint InhibiTors (ALERT)

Background: Immune-checkpoint inhibitors (ICI) can lead to immune-related adverse events (irAEs) in a significant proportion of patients. The mechanisms underlying irAEs development are mostly unknown and might involve multiple immune effectors, such as T cells, B cells and autoantibodies (AutoAb). Methods: We used custom autoantigen (AutoAg) microarrays to profile AutoAb related to irAEs in patients receiving ICI. Plasma was collected before and after ICI from cancer patients participating in two clinical trials (NCT03686202, NCT02644369). A one-time collection was obtained from healthy controls for comparison. Custom arrays with 162 autoAg were used to detect IgG and IgM reactivities. Differences of median fluorescent intensity (MFI) were analyzed with Wilcoxon sign rank test and Kruskal–Wallis test. MFI 500 was used as threshold to define autoAb reactivity. Results: A total of 114 patients and 14 healthy controls were included in this study. irAEs of grade (G) ≥ 2 occurred in 37/114 patients (32%). We observed a greater number of IgG and IgM reactivities in pre-ICI collections from patients versus healthy controls (62 vs 32 p < 0.001). Patients experiencing irAEs G ≥ 2 demonstrated pre-ICI IgG reactivity to a greater number of AutoAg than patients who did not develop irAEs (39 vs 33 p = 0.040). We observed post-treatment increase of IgM reactivities in subjects experiencing irAEs G ≥ 2 (29 vs 35, p = 0.021) and a decrease of IgG levels after steroids (38 vs 28, p = 0.009). Conclusions: Overall, these results support the potential role of autoAb in irAEs etiology and evolution. A prospective study is ongoing to validate our findings (NCT04107311)

Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials

Flow cytometry is a widely applied approach for exploratory immune profiling and biomarker discovery in cancer and other diseases. However, flow cytometry is limited by the number of parameters that can be simultaneously analyzed, severely restricting its utility. Recently, the advent of mass cytometry (CyTOF) has enabled high dimensional and unbiased examination of the immune system, allowing simultaneous interrogation of a large number of parameters. This is important for deep interrogation of immune responses and particularly when sample sizes are limited (such as in tumors). Our goal was to compare the accuracy and reproducibility of CyTOF against flow cytometry as a reliable analytic tool for human PBMC and tumor tissues for cancer clinical trials. We developed a 40+ parameter CyTOF panel and demonstrate that compared to flow cytometry, CyTOF yields analogous quantification of cell lineages in conjunction with markers of cell differentiation, function, activation, and exhaustion for use with fresh and viably frozen PBMC or tumor tissues. Further, we provide a protocol that enables reliable quantification by CyTOF down to low numbers of input human cells, an approach that is particularly important when cell numbers are limiting. Thus, we validate CyTOF as an accurate approach to perform high dimensional analysis in human tumor tissue and to utilize low cell numbers for subsequent immunologic studies and cancer clinical trials

Selinexor in Advanced, Metastatic Dedifferentiated Liposarcoma: A Multinational, Randomized, Double-Blind, Placebo-Controlled Trial

PURPOSE Antitumor activity in preclinical models and a phase I study of patients with dedifferentiated liposarcoma (DD-LPS) was observed with selinexor. We evaluated the clinical benefit of selinexor in patients with previously treated DD-LPS whose sarcoma progressed on approved agents. METHODS SEAL was a phase II-III, multicenter, randomized, double-blind, placebo-controlled study. Patients age 12 years or older with advanced DD-LPS who had received two-five lines of therapy were randomly assigned (2:1) to selinexor (60 mg) or placebo twice weekly in 6-week cycles (crossover permitted). The primary end point was progression-free survival (PFS). Patients who received at least one dose of study treatment were included for safety analysis (ClinicalTrials.gov identifier: ). RESULTS Two hundred eighty-five patients were enrolled (selinexor, n = 188; placebo, n = 97). PFS was significantly longer with selinexor versus placebo: hazard ratio (HR) 0.70 (95% CI, 0.52 to 0.95; one-sided P = .011; medians 2.8 v 2.1 months), as was time to next treatment: HR 0.50 (95% CI, 0.37 to 0.66; one-sided P < .0001; medians 5.8 v 3.2 months). With crossover, no difference was observed in overall survival. The most common treatment-emergent adverse events of any grade versus grade 3 or 4 with selinexor were nausea (151 [80.7%] v 11 [5.9]), decreased appetite (113 [60.4%] v 14 [7.5%]), and fatigue (96 [51.3%] v 12 [6.4%]). Four (2.1%) and three (3.1%) patients died in the selinexor and placebo arms, respectively. Exploratory RNA sequencing analysis identified that the absence of CALB1 expression was associated with longer PFS with selinexor compared with placebo (median 6.9 v 2.2 months; HR, 0.19; P = .001). CONCLUSION Patients with advanced, refractory DD-LPS showed improved PFS and time to next treatment with selinexor compared with placebo. Supportive care and dose reductions mitigated side effects of selinexor. Prospective validation of CALB1 expression as a predictive biomarker for selinexor in DD-LPS is warranted. (C) 2022 by American Society of Clinical Oncolog

Phase II randomised discontinuation trial of brivanib in patients with advanced solid tumours

Background: Brivanib is a selective inhibitor of vascular endothelial growth factor and fibroblast growth factor (FGF) signalling. We performed a phase II randomised discontinuation trial of brivanib in 7 tumour types (soft-tissue sarcomas [STS], ovarian cancer, breast cancer, pancreatic cancer, non-small-cell lung cancer [NSCLC], gastric/esophageal cancer and transitional cell carcinoma [TCC]). Patients and methods: During a 12-week open-label lead-in period, patients received brivanib 800 mg daily and were evaluated for FGF2 status by immunohistochemistry. Patients with stable disease at week 12 were randomised to brivanib or placebo. A study steering committee evaluated week 12 response to determine if enrolment in a tumour type would continue. The primary objective was progression-free survival (PFS) for brivanib versus placebo in patients with FGF2-positive tumours. Results: A total of 595 patients were treated, and stable disease was observed at the week 12 randomisation point in all tumour types. Closure decisions were made for breast cancer, pancreatic cancer, NSCLC, gastric cancer and TCC. Criteria for expansion were met for STS and ovarian cancer. In 53 randomised patients with STS and FGF2-positive tumours, the median PFS was 2.8 months for brivanib and 1.4 months for placebo (hazard ratio [HR]: 0.58, p = 0.08). For all randomised patients with sarcomas, the median PFS was 2.8 months (95% confidence interval [CI]: 1.4–4.0) for those treated with brivanib compared with 1.4 months (95% CI: 1.3–1.6) for placebo (HR = 0.64, 95% CI: 0.38–1.07; p = 0.09). In the 36 randomised patients with ovarian cancer and FGF2-positive tumours, the median PFS was 4.0 (95% CI: 2.6–4.2) months for brivanib and 2.0 months (95% CI: 1.2–2.7) for placebo (HR: 0.56, 95% CI: 0.26–1.22). For all randomised patients with ovarian cancer, the median PFS in those randomised to brivanib was 4.0 months (95% CI: 2.6–4.2) and was 2.0 months (95% CI: 1.2–2.7) in those randomised to placebo (HR = 0.54, 95% CI: 0.25–1.17; p = 0.11). Conclusion: Brivanib demonstrated activity in STS and ovarian cancer with an acceptable safety profile. FGF2 expression, as defined in the protocol, is not a predictive biomarker of the efficacy of brivanib

Early phase clinical trials to identify optimal dosing and safety.

The purpose of early stage clinical trials is to determine the recommended dose and toxicity profile of an investigational agent or multi‐drug combination. Molecularly targeted agents (MTAs) and immunotherapies have distinct toxicities from chemotherapies that are often not dose dependent and can lead to chronic and sometimes unpredictable side effects. Therefore utilizing a dose escalation method that has toxicity based endpoints may not be as appropriate for determination of recommended dose, and alternative parameters such as pharmacokinetic or pharmacodynamic outcomes are potentially appealing options. Approaches to enhance safety and optimize dosing include improved preclinical models and assessment, innovative model based design and dose escalation strategies, patient selection, the use of expansion cohorts and extended toxicity assessments. Tailoring the design of phase I trials by adopting new strategies to address the different properties of MTAs is required to enhance the development of these agents. This review will focus on the limitations to safety and dose determination that have occurred in the development of MTAs and immunotherapies. In addition, strategies are proposed to overcome these challenges to develop phase I trials that can more accurately define the recommended dose and identify adverse events

Radiomics in sarcoma trials: a complement to RECIST for patient assessment

Radiological imaging has a critical role in the diagnosis of sarcomas and in evaluating therapy response assessment. The current gold standard for response assessment in solid tumors is the Response Evaluation Criteria in Solid Tumors, which evaluates changes in tumor size as a surrogate endpoint for therapeutic efficacy. However, tumors may undergo necrosis, changes in vascularization or become cystic in response to therapy, with no significant volume changes; thus, size assessments alone may not be adequate. Such morphological changes may give rise to radiographic phenotypes that are not easily detected by human operators. Fortunately, recent advances in high-performance computing and machine learning algorithms have enabled deep analysis of radiological images to extract features that can provide richer information about intensity, shape, size or volume, and texture of tumor phenotypes. There is growing evidence to suggest that these image-derived or “radiomic features” are sensitive to biological processes such as necrosis and glucose metabolism. Thus, radiomics could prove to be a critical tool for assessing treatment response and may present an integral complement to existing response criteria, opening new avenues for patient assessment in sarcoma trials