Citations

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.

7530 Background: B-cell maturation antigen (BCMA)-directed CAR-T therapy is a highly effective treatment with potential for long-term remission of RRMM with improved outcomes vs daratumumab-based regimens. CAR-T suitability may be limited by delays/manufacturing failure and changes in pt condition, financial situation, or access to specialized treatment centers. We characterized pts with RRMM who underwent leukapheresis to prepare for CAR-T and pt treatment waiting time, to understand the delays incurred prior to CAR-T infusion. Methods: A real-world study in the US using the PharMetrics Plus claims database (July 2020–September 2024) was performed. Eligible pts were aged ≥18 years with RRMM, had their first leukapheresis claim (index date) during the index period (March 2021–August 2024), were continuously enrolled in medical/pharmacy benefits for ≥180 days prior to and ≥30 days after index date, and were not clinical trial participants. Pt characteristics, rate of CAR-T receipt, and time to CAR-T receipt were assessed during the variable follow-up. Results: Overall, 337 pts who received leukapheresis were identified. Median (interquartile range) age was 62 (55–67) years, and 59.1% of pts were male. Mean National Cancer Institute (NCI) comorbidity index was 0.52. Most pts received care in the Midwest (31.8%) or Northeast (24.6%) regions. Most pts (95.9%) received leukapheresis in an outpatient setting. Median available follow-up time was 255 days (mean 337 days). During follow-up, 183 pts (54.3%) did not receive CAR-T after leukapheresis. Among pts who had ≥12 months of follow-up (n=143), the proportion of pts who did not receive CAR-T was similar at 53.1% (n=76). Among pts who received CAR-T during the follow-up (n=154; 45.7%), the median waiting time was 54.0 days (mean 56.7 days). The cohort of pts who did not receive CAR-T had a higher proportion of females (48.6%/31.8%), were more likely to have Medicaid (29.5%/7.8%), had higher NCI comorbidity index (scores 0.55/0.47), and most commonly received care in the Northeast (35.5%/11.7%) while pts who received CAR-T most commonly received care in the Midwest (23.0%/42.2%). The most common first classes of multiple myeloma medications after leukapheresis but prior to CAR-T infusion in pts who did/did not receive CAR-T were an alkylating agent (65.6%/36.1%), proteasome inhibitor (40.3%/30.6%), steroid (38.3%/39.3%), immunomodulatory drug (22.7%/27.3%), anti-CD38 (20.8%/20.8%), and stem cell transplant (0%/17.5%). Conclusions: Over half of pts receiving leukapheresis did not receive CAR-T infusion, highlighting a persistent gap between eligibility and treatment delivery. These data emphasize an urgent need for broadly accessible BCMA-targeted therapies that provide deep and durable responses.

1617 Background: Large language models (LLMs) are increasingly evaluated for oncology decision support, yet guideline concordance and hallucinations remain variable across cancer types and tasks. Retrieval-augmented generation (RAG) may improve safety by constraining the evidence base used during generation. Prior oncology LLM studies report discordance with guideline-based care, but few describe a reproducible health-system “vetting mechanism” that couples guideline-only retrieval with clinician-scored safety rubrics across multiple oncology domains. Methods: We assembled 316 de-identified oncology cases in a tumor-board template: medical oncology (breast, GI, GU, leukemia; n=216), gynecologic oncology (n=50), and neuro-oncology/CNS metastatic cancer (n=50). Each case was run on three systems: baseline GPT-4 (O1), NCCN-guideline anchored RAG (O2; guideline-constrained retrieval), and a literature-anchored system (OpenEvidence; O3). Oncologists independently scored outputs using a modified Generative Performance Score (mGPS; −1 to +1) combining guideline concordance with hallucination penalties; readability/rationality was scored on a 1–5 Likert scale. Inter-rater reliability was assessed (κ =.63). This workflow was designed as a pre-deployment model credentialing step for health-system governance (source policy → case bank → clinician-scored acceptance criteria). Results: Across all 316 cases (case-averaged), O2 achieved the highest mean total mGPS (0.75) compared with O1 (0.43) and O3 (0.59). In the medical oncology cohort (n=216), O2 outperformed O1 and O3 (mean mGPS 0.72 vs 0.35 and 0.54), with higher guideline concordance (0.85 vs 0.68 and 0.75) and lower hallucination penalties (−0.09 vs −0.25 and −0.19); readability was also highest for O2 (4.17 vs 3.22 and 3.86). In gynecologic oncology (n=50), O2 again performed best (mGPS 0.827) relative to O3 (0.697) and O1 (0.647). In the CNS cohort (n=50), mean composite mGPS favored O2 (0.82) over O3 (0.68) and O1 (0.55), while readability remained stable across models (3.55–3.73). Severe hallucination/unsafe recommendation rate: O1 [26%], O2 [5%], O3 [17%] (pre-specified definition; e.g., major fabricated citation or guideline-discordant harmful recommendation). Conclusions: In a 316-case, multi-domain clinician-scored evaluation, limiting retrieval to oncology guideline sources improved safety-aligned performance relative to unconstrained generation or literature-only anchoring. A guideline-constrained RAG configuration paired with clinician-scored rubrics and tail-risk reporting can serve as a practical health-system vetting mechanism for governance, re-validation, and change control of LLM decision support, including in resource-limited settings where reliable guideline-consistent recommendations are essential.

Abstract While the use of NCCN guidelines is essential in lymphoma care, adherence among oncologists remains highly variable. Real-world concordance ranges from 65–80% overall, dropping to as low as 11–46% for certain lymphoma subtypes. Although 88% of clinicians report using NCCN guidelines for Hodgkin lymphoma, adherence to PET-adapted approaches is inconsistent. This gap highlights a critical need for tools that support guideline-concordant care.Large language models (LLMs) such as GPT–4 promise decision support, yet hallucinations and divergence from evidence-based care remain obstacles. We assessed whether retrieval-augmented generation (RAG) with National Comprehensive Cancer Network (NCCN) guidelines improves GPT–4 performance in lymphoma treatment recommendation tasks. To address this, we developed a RAG-enhanced GPT-4 agent (RAG-GPT) by indexing the 2025 NCCN Lymphoma Guidelines into a vector database. Fifty clinical vignettes spanning diverse lymphoma subtypes were compiled from published case reports; this sample provides 80 % power (α = 0.05) to detect a mean score difference of ≥ 0.18 on the modified Generative AI Performance Score (mG-PS). Guideline concordance was assessed based on NCCN guidelines and clinical judgment reflecting real-world oncological decision-making. Scores were normalized between -1.0 and 1.0 for consistent evaluation. Readability and rationale clarity were rated on a 5-point Likert scale. Baseline GPT-4 and RAG-GPT generated treatment plans via standardized prompts. An oncologist scored all outputs with mG-PS, which rates NCCN concordance (0.0–1.0) and penalizes hallucinations (-0.25 to –1.0). RAG-GPT achieved a higher mean mG-PS (0.32) than baseline GPT–4 (0.10) (t(98)=-2.11, p=0.038; mean score difference ΔmG-PS=0.22). Importantly, RAG-GPT eliminated severe hallucinations, reinforcing confidence in the safety of its treatment reccomendations. Fully concordant “gold-standard” plans were produced in 46 % of vignettes with RAG-GPT versus 22 % with baseline. Readability scores favored RAG-GPT modestly without statistical significance. Lymphoma's bimodal age distribution, heterogeneity, and lack of standardized treatment algorithms, coupled with regimen toxicity and the need to account for patient frailty and comorbidities, create complex clinical scenarios that constrain LLM applicability for nuanced decision-making. Non-Hodgkin lymphoma, particularly relapsed/refractory, transformed disease, or rare subtypes, lacks a defined standard of care, leading reviewers to rely on clinical judgment or published case series beyond NCCN guidelines. This underscores a key limitation of LLM-supported tools: their performance is highly dependent on the presence of clear algorithmic consensus within a given disease. Linking GPT-4 to NCCN guidelines via RAG significantly improved treatment accuracy and reduced hallucination severity in lymphoma, though performance remained lower than prior RAG studies in cancers like breast and CNS suggesting a greater impact on hallucination reduction than on retrieval of a consistent gold standard regimen. These findings support continued development and prospective validation of guideline-anchored LLMs for clinical use.

Refractory ascites often requires therapeutic paracentesis, which is associated with potential risks and diminished quality of life. Terlipressin is a vasopressin analog that is indicated for i.v. bolus injection for hepatorenal syndrome, with the potential to reduce large-volume ascites and its complications. Continuous infusion of terlipressin is associated with fewer adverse effects than bolus dosing. The efficacy and safety of continuous infusion of a novel liquid formulation of terlipressin acetate (BIV201) were evaluated in this open-label phase 2 study. Patients with cirrhosis and refractory ascites were randomly assigned (2:1) to receive two 28-day cycles of continuous infusion BIV201 plus standard of care (SOC) separated by a ≤56-day washout (n=10), or SOC alone (n=5). Data analysis was limited by the small sample size and confounded by a potential interaction with gabapentinoids in the BIV201+SOC group. Nonetheless, there were differences in favor of BIV201+SOC versus SOC in the coprimary efficacy endpoints and several quality of life assessments. The beneficial effects of BIV201 on liver complications (mean: 90% CI; BIV201-completers=2.87: 1.51; 5.46 vs. SOC=2.38: 1.20; 4.73) and the change in cumulative ascites (mean: 90% CI; BIV201-completers=−10.76: −26.51; 5.00 vs. SOC=−4.99: −21.95; 11.97) were more pronounced versus SOC in the 5 BIV201+SOC patients who completed both treatment cycles. There were also greater improvements in exploratory quality of life assessments and the percent change in therapeutic paracenteses with BIV201+SOC (−27.94±41.80) versus SOC (−16.67±45.64). Despite the high rate of hyponatremia in the BIV201+SOC group (4/10 patients), the safety profile suggested that continuous BIV201 infusion was well tolerated. These findings support further development of BIV201 in confirmatory trials.

PURPOSE To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data. METHODS This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773 ), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities. RESULTS A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased. CONCLUSION Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.

Abstract Introduction: Belantamab mafodotin (belamaf) is a B-cell maturation antigen (BCMA)-targeting antibody-drug conjugate approved in the United States (US) and Europe as a monotherapy for triple-class refractory adult patients with relapsed or refractory multiple myeloma (RRMM). This hypothesis-generating post-hoc analysis of DREAMM-2 trial (NCT03525678) data examined relationships between corneal exam findings (superficial punctate keratopathy and/or microcyst like epithelial changes), best-corrected visual acuity (BCVA), and direct patient-reported ocular symptoms per the Ocular Surface Disease Index (OSDI) questionnaire. This approach could provide insight into the relationship between corneal exam findings, BCVA, and ocular symptoms and their impact on quality of life to help determine if BCVA decline and symptoms could guide dosing. Currently, in the US, eye exams with eye-care professionals (ECPs) are required for each belamaf treatment, which can add to patients' therapy-related burden. Identifying a surrogate marker for results from corneal exam findings would help providers determine if dosing adjustments are necessary. Methods: Eye exams (including a corneal exam and BCVA assessment using the Snellen chart) were performed on all patients receiving single-agent belamaf (2.5 mg/kg) by ECPs at baseline (BL) and before each dose, administered q3w. Corneal exam findings (keratopathy) and BCVA were assessed per protocol-defined criteria, and grade (GR) assessment was based on the worst finding in the worse eye. BCVA and keratopathy grading were relative to BL. Patient-reported ocular symptoms and vision-related functioning, as per the OSDI, were used to evaluate the impact of treatment-related ocular toxicity. The OSDI is a patient-reported outcome questionnaire that assesses eye symptoms and effects on vision-related function in the past week and was performed in all patients before each belamaf dose. Items 1-5 address the frequency of eye-related symptoms (eg, painful or sore eyes, blurred vision) while items 6-9 address the frequency of functional limitations (eg, driving at night or reading). OSDI was considered positive, clinically meaningful, and potentially associated with treatment when at least one question 1-5 (ie, sensitivity to light, gritty or painful eyes, blurred or poor vision) was reported as experienced "all of the time" and at least one question 6-9 (ie, driving at night, reading, working with PC, or watching TV) was reported as experienced "most of the time." Results: Overall, GR 3-4 (severe) keratopathy was observed only 5% of the time in patients who did not report frequent ocular symptoms as measured by the OSDI questionnaire (no items 1-9 ≥ "most of the time"). A further breakdown of the questions revealed that, in patients who reported no items 1-5 "all of the time" AND no items 6-9 ≥ "most of the time" (OSDI negative), GR 3-4 keratopathy was observed only 6% of the time (Table). When considering individual OSDI items, in patients who reported "no deterioration from BL" for any of the OSDI eye-related symptoms or functional limitations, GR 3-4 keratopathy was observed ~3%-7% of the time while GR 0-2 (mild) keratopathy was observed ~23%-34% of the time. Similar results were observed in patients with BCVA ≤20/30 at BL who reported "no deterioration from BL" for any eye-related adverse events (AEs) or functional limitations (GR 3-4 keratopathy: ~2%-6% of the time; GR 0-2 keratopathy: ~19%-28% of the time); patients with worse visual acuity at BL (BCVA >20/30) who reported "no deterioration" for items 1-9 had lower incidence of GR 3-4 and GR 0-2 keratopathy (~0%-1% and ~3%-8%, respectively). Conclusions: The results from this hypothesis-generating post-hoc analysis suggest that hematologists/oncologists may be able to use clinical indicators/tools (eg, ocular symptoms, the OSDI tool) as potential surrogate markers for eye exam results to help determine whether dosing changes are needed. This approach will be explored in trials with other belamaf dose regimens that are currently under evaluation. If the results are validated, it may be possible for hematologists/oncologists to manage belamaf dose modifications, due to ocular AEs, without a confirmatory eye exam by the ECP, reducing the patient burden. Funding Source: GSK (205678). Drug linker technology licensed from Seagen; monoclonal antibody produced using POTELLIGENT Technology licensed from BioWa. Figure 1 Figure 1. Disclosures Popat: Takeda: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; AbbVie, BMS, Janssen, Oncopeptides, and Amgen: Honoraria; Abbvie, Takeda, Janssen, and Celgene: Consultancy; Janssen and BMS: Other: travel expenses. Badros: GlaxoSmithKline: Research Funding; BMS: Research Funding; Janssen: Research Funding; J&J: Research Funding. Kumar: Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; BMS: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Oncopeptides: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Merck: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Rodríguez-Otero: Regeneron: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel and other expenses. Cohen: BMS: Research Funding; GlaxoSmithKline: Other: Personal fees and other association with, Research Funding; Novartis: Research Funding; Celgene: Honoraria, Other: Personal fees and other association with; Janssen: Honoraria; Kite Pharma: Honoraria; Oncopeptides: Honoraria; Seattle Genetics: Honoraria; Takeda: Honoraria. Manier: Regeneron: Consultancy, Research Funding; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Research Funding; Celgene - Bristol Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Voorhees: Secura Bio: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Gay: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Bluebird Bio: Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria. Rifkin: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Fresenius-Kabi: Membership on an entity's Board of Directors or advisory committees; Coherus: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb (Celgene): Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; McKesson: Current Employment, Current equity holder in publicly-traded company; Karyopharm: Membership on an entity's Board of Directors or advisory committees. Martin: Janssen: Research Funding; Sanofi: Research Funding; Oncopeptides: Consultancy; Amgen: Research Funding; GlaxoSmithKline: Consultancy. Chari: Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; Secura Bio: Consultancy, Membership on an entity's Board of Directors or advisory committees; Shattuck Labs: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Research Funding; Pharmacyclics: Research Funding; Millenium/Takeda: Consultancy, Research Funding; Sanofi Genzyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS/Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weisel: Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; Novartis: Honoraria; Pfizer: Honoraria. Farooq: Five Prime Therapeutics/Amgen: Consultancy; GlaxoSmithKline: Consultancy. Jeng: OysterPoint: Consultancy; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy; Eyegate: Current equity holder in publicly-traded company. Chng: Abbvie: Consultancy; GlaxoSmithKline: Consultancy; Takeda: Consultancy; BMS/Celgene: Consultancy, Research Funding; Sanofi: Consultancy; Novartis: Research Funding; Johnson & Johnson: Consultancy, Research Funding; Amgen: Consultancy; Antengene: Consultancy; Pfizer: Consultancy; Aslan: Research Funding. Lee: Takeda Pharmaceuticals: Consultancy, Research Funding; Oncopetides: Consultancy; Sanofi: Consultancy; GlaxoSmithKline: Consultancy, Research Funding; Legend Biotech: Consultancy; Karyopharm: Consultancy; Janssen: Consultancy, Research Funding; Genentech: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding; Regeneron: Research Funding. Berdeja: BMS: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Celularity: Research Funding; CRISPR Therapeutics: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Kite Pharma: Consultancy; Legend Biotech: Consultancy; SecuraBio: Consultancy; EMD Serono: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding; Ichnos Sciences: Research Funding; Incyte: Research Funding; Novartis: Research Funding; Poseida: Research Funding; Bluebird bio: Consultancy, Research Funding; Astex Pharmaceuticals: Research Funding; Amgen: Research Funding; Abbvie: Research Funding; Takeda: Consultancy; Sanofi: Research Funding; Acetylon: Research Funding; Teva: Research Funding; Lilly: Research Funding. Jadhav: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Tosolini: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Eliason: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Palumbo: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Dimopoulos: Beigene: Honoraria; BMS: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Takeda: Honoraria. Lonial: Merck: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; BMS/Celgene: Consultancy, Honoraria, Research Funding; AMGEN: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Trudel: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Amgen Canada: Honoraria; Sanofi: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Research Funding; Karyopharm: Honoraria. Richardson: Sanofi: Consultancy; Celgene/BMS: Consultancy, Research Funding; Protocol Intelligence: Consultancy; GlaxoSmithKline: Consultancy; Secura Bio: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy, Research Funding; Janssen: Consultancy; Oncopeptides: Consultancy, Research Funding; Regeneron: Consultancy; AstraZeneca: Consultancy; Takeda: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding. Terpos: Celgene: Consultancy, Honoraria, Research Funding; Genesis: Consultancy, Honoraria, Research Funding; GSK: Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; BMS: Honoraria; Janssen-Cilag: Consultancy, Honoraria, Research Funding; Novartis: Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding.