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1596 A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable Measurable Residual Disease (MRD) in Transplant-Eligible Multiple Myeloma (MM)

Program: Oral and Poster Abstracts
Session: 651. Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational: Poster I
Hematology Disease Topics & Pathways:
Clinically Relevant, Immunology, Biological Processes, Technology and Procedures, Machine Learning
Saturday, December 11, 2021, 5:30 PM-7:30 PM

Camila Guerrero1*, Noemi Puig, MD, PhD2, María Teresa Cedena, MD, PhD3*, Ibai Goicoechea, PhD1, Cristina Pérez Ruiz1*, Juan José Garcés1*, Cirino Botta, MD, PhD4, Maria Jose Calasanz, PhD, BSc1*, Norma C. Gutierrez2*, Maria Luisa Martin-Ramos3*, Albert Oriol5*, Rafael Rios, MD, PhD6*, Miguel Hernández7*, Rafael Martínez, M.D.8*, Joan Bargay9*, Felipe De Arriba, PhD10*, Luis Palomera, MD, PhD11*, Ana Pilar Gonzalez, PhD12*, Adrián Mosquera Orgueira13*, Marta Sonia Gonzalez, MD13*, Joaquín Martínez-López3*, Juan Jose Lahuerta, MD, PhD3*, Laura Rosinol14*, Joan Bladé Creixenti15, Maria-Victoria Mateos2, Jesus San-Miguel1 and Bruno Paiva, PhD1*

1Clinica Universidad de Navarra, Centro de Investigacion Medica Aplicada (CIMA), Instituto de Investigacion Sanitaria de Navarra (IDISNA), CIBER-ONC number CB16/12/00369, Pamplona, Spain
2Hospital Universitario de Salamanca Hematología. Instituto de investigación biomédica de Salamanca (IBSAL), Salamanca, Spain
3Hospital Universitario 12 de Octubre, Madrid, Spain
4Hematology Unit, "Annunziata" Hospital of Cosenza, Cosenza, Italy
5Institut Català d'Oncologia L’Hospitalet, Barcelona, Spain
6Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria, Granada, Spain
7Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain
8Hospital Universitario San Carlos, Madrid, Spain
9Hospital Universitario Son Llatzer, Institut d’ investigacio Illes Balears (IdISBa), Palma de Mallorca, Spain
10Hospital Morales Meseguer, IMIB-Arrixaca, Universidad de Murcia, Murcia, Spain
11Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
12Hospital Central de Asturias, Oviedo, Spain
13Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), SERGAS, Santiago de Compostela, Spain
14Hematology Department, IDIBAPS, Hospital Clínic, Barcelona, Spain
15Amyloidosis and Multiple Myeloma Unit, Department of Hematology, IDIBAPS, Hospital Clínic, Barcelona, Spain, Barcelona, Spain

INTRODUCTION: There is expectation of using biomarkers to personalize treatment in MM. Yet, a successful treatment selection cannot be confirmed before 5 or 10 years of progression-free survival (PFS). Treatment individualization based on the probability of an individual patient to achieve undetectable MRD with a singular regimen, could represent a new model towards personalized treatment with fast assessment of its success. This idea has not been investigated previously.

AIM: Develop a machine learning model to predict undetectable MRD in newly-diagnosed transplant-eligible MM patients, treated with a standard of care.

METHODS: This study included a total of 278 newly-diagnosed and transplant-eligible MM patients treated with proteasome inhibitors, immunomodulatory drugs and corticosteroids. The training (n=152) and internal validation cohort (n=60) consisted of 212 active MM patients enrolled in the GEM2012MENOS65 trial. The external validation cohort was defined by 66 high-risk smoldering MM patients enrolled in the GEM-CESAR trial, which treatment differed only by the substitution of bortezomib by carfilzomib during induction and consolidation.

RESULTS: We started by investigating patients’ MRD status after VRD induction, HDT/ASCT and VRD consolidation according to their ISS and R-ISS, LDH levels, and cytogenetic alterations. Surprisingly, neither the ISS nor the R-ISS predicted significantly different MRD outcomes. Indeed, high LDH levels and del(17p13) were the only parameters associated with lower rates of undetectable MRD. Because these two features are relatively infrequent at diagnosis, we next aimed to evaluate other disease features and develop integrative, weighted and more effective models based on machine learning algorithms.

Of 37 clinical and biological parameters evaluated, 17 were associated with MRD outcomes. These were subsequently modeled using logistic regression for machine learning classification, where the sum of the weighted coefficients multiplied by its input variable, is transformed into a probability outcome that ranges from 0 to 1 using a logit sigmoid function. The most effective model resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (plasma cell [PC] clonality in bone marrow and CTCs in blood) and immune related (myeloid precursors, mature B cells, intermediate neutrophils, eosinophils, CD27negCD38pos T cells and CD56brightCD27neg NK cells) biomarkers. Of note, immune biomarkers displayed the highest coefficient weights and were determinant to predict patients’ MRD status in this model.

Data obtained for an individual patient can be substituted into our formula, which results in a numerical probability of achieving undetectable (>0.5) vs persistent (<0.5) MRD after treatment. If probability outcomes are >0.685 or <0.365 (observed in 102/212 patients), MRD outcomes are respectively predicted with higher confidence. Standard-confidence, high-confidence, and external validation predictions were accurate in 152/212 (71.7%), 85/102 (83.3%), and 48/66 (72.7%) patients respectively. Similarly, the external validation set exhibited a similar receiver operating characteristic (ROC) curve as the internal test set (AUC of 0.73 and 0.77 respectively).

Patients predicted to achieve undetectable MRD using standard and high-confidence values showed longer PFS and overall survival (OS) than those with probability of persistent MRD. In fact, patients with >0.687 probability of achieving undetectable MRD showed 86% PFS and 94% OS at five years, whereas those in whom persistent MRD was predicted (<0.365), median PFS was 44 months, and 69% OS was observed at five years. These data indicate that the combination of cytogenetics, tumor burden in bone marrow plus peripheral blood, and immune profiling, may also be explored to identify a subset of patients that have a singular disease biology and long-term survival.

CONCLUSION: We demonstrated that it is possible to predict patients’ MRD status with significant accuracy, using an integrative, weighted model based on machine learning algorithms. Although immune biomarkers are not commonly used, the raw data from which these can be developed is generally obtained in diagnostic laboratories using flow cytometry to screen for PC clonality. Furthermore, we made the model available to facilitate its use in clinical practice at www.MRDpredictor.com.

Disclosures: Puig: Celgene, Janssen, Amgen, Takeda: Research Funding; Celgene: Speakers Bureau; Amgen, Celgene, Janssen, Takeda: Consultancy; Amgen, Celgene, Janssen, Takeda and The Binding Site: Honoraria. Cedena: Janssen, Celgene and Abbvie: Honoraria. Oriol: Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, 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; BMS/Celgene: 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. De Arriba: Amgen: Consultancy, Honoraria; Glaxo Smith Kline: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; BMS-Celgene: Consultancy, Honoraria, Speakers Bureau. Martínez-López: Janssen, BMS, Novartis, Incyte, Roche, GSK, Pfizer: Consultancy; Roche, Novartis, Incyte, Astellas, BMS: Research Funding. Lahuerta: Celgene: Other: Travel accomodations and expenses; Celgene, Takeda, Amgen, Janssen and Sanofi: Consultancy. Rosinol: Janssen, Celgene, Amgen and Takeda: Honoraria. Bladé Creixenti: Janssen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. Mateos: Sea-Gen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: 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; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: 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; Celgene - Bristol Myers Squibb: 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; Bluebird bio: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria. San-Miguel: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Merck Sharpe & Dohme, Novartis, Regeneron, Roche, Sanofi, SecuraBio, and Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. Paiva: Bristol-Myers Squibb-Celgene, Janssen, and Sanofi: Consultancy; Adaptive, Amgen, Bristol-Myers Squibb-Celgene, Janssen, Kite Pharma, Sanofi and Takeda: Honoraria; Celgene, EngMab, Roche, Sanofi, Takeda: Research Funding.

*signifies non-member of ASH