Evolving Therapeutic Approaches for Older Patients with Acute Myeloid Leukemia in 2021
Abstract
:Simple Summary
Abstract
1. Introduction
2. Fitness Assessment
2.1. FIT, UNFIT, FRAIL
2.2. Early Mortality and Survival Predictive Models
2.3. Geriatric Assessment
2.4. Comorbidity and Organ Function Scores
Score | N° of Patients | Score Variables | Specificities |
---|---|---|---|
Early mortality and survival predictive models | |||
Malfuson et al. (2008) [26] | 416 | Age, WBC, PS, cytogenetic risk | Predictive of mortality or survival, not proper fitness scores, therapy-specific, inclusive of disease features |
Wheatley et al. (2009) [21] | 2483 | Age, WBC, PS, cytogenetic risk, type of leukemia (de novo vs. secondary) | |
Kantarjian et al. (2010) [23] | 446 | Age, PS, cytogenetic risk, creatinine | |
Krug et al.—AML Score (2010) [24] | 1406 | Age, body temperature, Hb, platelet count, fibrinogen, type of leukemia (de novo vs. secondary) | |
Walter et al.—TRM Score (2011) [22] | 3365 | Age, PS, platelet count, WBC, peripheral blood blast percentage, albumin, creatinine, type of leukemia (de novo vs. secondary) | |
Geriatric assessment scores | |||
Soubeyran et al.—G8 Score (2008) [30] Soubeyran et al. (2011) [31] | 364, 1668 | Seven Mini Nutritional Assessment (MNA) items (appetite, weight loss, motricity, BMI, cognition and depression, self-related health, medications), age | Quick and easy to apply, generalizability for cancer patients |
Deschler et al. (2013) [33] | 195 | PS (Karnofsky index), activities of daily living (ADL) and QoL/fatigue | Time consuming |
Klepin et al. (2013) [35] | 74 | Cognition, psychological function, physical function, comorbidity | |
Sherman et al. (2013) [34] | 101 | Comorbidity, physical function, pain | |
Comorbidity and organ function scores | |||
Sorror et al.—HCT-CI Score (2005) [37] | 1055 (+ 347) | Comorbidities | Originally developed to assess eligibility for HCT |
Sorror et al.—AML Composite Model (2017) [39] | 733 (+ 367) | Age, augmented HCT-CI and cytogenetic/molecular risks | Inclusive of disease features, possibly able to identify patients who do not benefit from intensive chemotherapy |
Ferrara et al. (2013) [40]; Borlenghi et al. (2021) [11] | 699 | Age, PS, comorbidities (cardiac, pulmonary, renal, hepatic, infections, mental illness, uncontrolled neoplasia) | Easily and widely applicable, not inclusive of disease features, able to predict benefit from more or less intensive treatments in different fitness groups (fit/unfit/frail patients) |
3. Intensive Approaches for FIT Patients
3.1. Induction Chemotherapy
3.1.1. Intensifying Standard Induction Chemotherapy
3.1.2. New Drug Formulations
3.1.3. Incorporating Antibody–Drug Conjugates
3.1.4. Incorporating Targeted Agents
3.2. Consolidation Chemotherapy
3.3. Autologous Stem Cell Transplantation
3.4. Maintenance Therapy
3.5. Allogeneic Hematopoietic Cell Transplantation
3.5.1. Indications
3.5.2. Outcome of Transplantation in Older Patients with AML
3.5.3. Prognostic Models
4. Less-intensive Approaches
4.1. Venetoclax-based Combinations
4.2. Glasdegib
4.3. FLT3 Inhibitors
4.4. IDH Inhibitors
4.5. Future Perspectives
4.5.1. APR-246
4.5.2. Pevonedistat
4.5.3. New HMAs
4.5.4. New Families of Epigenetic Drugs
4.5.5. HMA and Venetoclax Backbone
4.5.6. JAK2 Inhibitors
4.5.7. Immunotherapy
Agent | Target | Study Phase | Association | AML Population | Age in Study, Median (Range) | cCR in AML | References |
---|---|---|---|---|---|---|---|
Eprenetapopt | p53 | Ib/II | Azacitidine | TP53-mutated MDS/AML | 66 (34–85) | 54% | [174] |
II | Azacitidine | Untreated TP53-mutated MDS/AML | 74 (44–87) | 17% | [175] | ||
Pevonedistat | NEDD8 activating enzyme | II | Azacitidine | Higher risk MDS/low-blast AML | 72 (34–91) | 41% | [177] |
II | Azacitidine + venetoclax | Newly diagnosed unfit AML | NA | NA | NCT04266795 | ||
III | Azacitidine | Higher risk MDS/low-blast AML | NA | NA | NCT03268954 | ||
Guadecitabine | Aberrant DNA methylation | II | Alone | Newly diagnosed unfit AML | 77 (62–92) | 54% | [178] |
I | Alone | R/R MDS/AML | 68 (36–86) | 6% | [179] | ||
III | Alone | Untreated unfit AML | 76 | 19.4% | NCT02348489 | ||
ASTX727 | Aberrant DNA methylation | III | Alone | MDS/ low blast AML | NA | NA | NCT03306264 |
I | Venetoclax | Newly diagnosed unfit AML | NA | NA | NCT04657081 | ||
Pracinostat | Histone deacetylase | II | Azacitidine | Newly diagnosed unfit AML | 75 (66–84) | 52% | [182] |
III | Azacitidine | Newly diagnosed unfit AML | NA * | NA * | NCT03151408 | ||
OTX015 | BET | I | Alone | R/R AML | 70 (60–75) | 7% | [183] |
Pinometostat | DOT1L | I | Alone | Advanced AML | 50 (19–81) | 4% | [185] |
Iadademstat | LSD1 | I | Alone | R/R AML | 67 (30–81) | 3.7% | [186] |
II | Azacitidine | Newly diagnosed unfit AML | NA | NA | EudraCT 2018-000482-36 |
5. Precision Medicine
6. Quality of Life and Palliative Care
6.1. Older AML Patients’ Needs
6.2. Health-Related Quality of Life Assessment
6.3. Integrated Palliative and Oncology Care
6.4. Supportive Care
6.5. End-of-Life Care
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Reference | # Patients Diagnosis | Age | Allo-HCT | Graft Source | Conditioning | GVHD Acute II-IV | GVHD Chronic | LFS | OS | NRM |
---|---|---|---|---|---|---|---|---|---|---|
Bertz, JCO 2003 [101] | 19 AML | 60–70 | MSD, n = 7; MUD, n = 12 | PBSC | RIC | 59% | 65% | 1 y 68% | 1 y 61% | 1 y 22% |
Wong, Blood 2003 [102] | AML/MDS, n = 20 CML, n = 9 | ≥55 | MUD | BM | RIC | 41% | 63% | 1 y 37% | 1 y 44% | 1 y 55% |
Yanada, BMT 2004 [103] | 91 AML | ≥50 | MSD 80%, Alternative 20% | BM 74%, PBSC 21% | - | - | - | 1 y 34% | 1 y 35% | 100 d 16% |
Gupta, BBMT 2005 [104] | 24 AML/MDS | ≥60 | MSD | PBSC | RIC | 45% | 74% | 2 y 44% | 2 y 52% | 2 y 25% |
Spyridonidis, Blood 2005 [105] | 34 AML/MDS | ≥60 | MUD | BM 12%, PBSC 88% | RIC | - | - | 2 y 53% | 2 y 62% | 2 y 20% |
McClune, JCO 2010 [106] | 63 AML/MDS | ≥65 | MSD 51%, MUD 49% | PBSC 97% | RIC | 33% | 53% | 2 y 34% | 2 y 36% | 2 y 34% |
Alatrash, BBMT 2011 [107] | AML, n = 63; MDS, n = 16 | ≥55 | MSD 52%, MUD 48% | BM 48%, PBSC 52% | MAC, (Bu-Flu) | 40% | 43% | 2 y 44% | 2 y 46% | 100 d 6%, 1 y 19% |
De Latour, BMT 2015 [108] | 714 AML | ≥55 | MSD, n = 404; MUD, n = 310 | PBSC | RIC | MSD: 17%, MUD: 25% | MSD: 46%, MUD: 47% | 3 y MSD:46%, MUD: 47% | 3 y MSD: 49%, MUD: 49% | 3 y MSD:17%, MUD: 23% |
Kasamon, JCO 2015 [109] | 271 AML/MDS | 50–75 | haplos | BM | RIC | 33% | 10% | 3 y 40% | 3 y 49% | 1 y 12% |
Haen, Blood Cancer J 2016 [110] | 56 AML | ≥70 | MSD, n = 7; MUD, n = 49 | PBSC, n = 55; BM, n = 1 | MAC, RIC | 16% | 32% | 3 y 43% | 3 y 42% | 2-y18% |
Pohlen, BMT 2016 [111] | 187 AML/MDS | ≥60 | MSD, MUD | PBSC 94%, BM 4% | RIC | - | - | 3 y 32% | 3 y 35% | 1 y 37% |
Bertz, Leukemia 2016 [112] | 250 AML/MDS | ≥60 | MSD, n = 64; MUD, n = 186 | PBSC | RIC | 23% | 37% | 2 y 40% | 2 y 48% | 2 y 29% |
Slade, BBMT 2017 [113] | 95 AML/MDS | 55–65 (*), ≥65 (#), ≥65 (§) | Haplos (*), Haplos (#), MUD (§) | PBSC | MAC, RIC | (*) 34%, (#) 14%, (§) 27% | (*) 35%, (#) 9%, (§) - | (*) -, (#) -, (§) - | (*) 2 y 34%, (#) 2 y 15%, (§) 2 y 24% | (*) 2 y 39% (#) 2 y 32% (§) 2 y 55% |
Ringden, BBMT 2019 [114] | AML, n = 713; n = 16,161 | ≥70 (*), 50–69 (#) | MSD, MUD | PBSC, BM | RIC, MAC | (*) 23%, (#) 25% | (*) 43%, (#) 41% | (*)2 y 33%, (#) 2 y 44% | (*) 2 y 38%, (#) 2 y 50% | (*) 2 y 34% (#) 2 y 24% |
AML Subtype | Drug | Approval | Indication | Regimen | Phase Trial | cCR (%) | Median OS (months) |
---|---|---|---|---|---|---|---|
FLT3-mut | Midostaurin | FDA, EMA | ND AML | With 7+3 (DNR) | III | 59 | (51% at 4 years) |
Gilteritinib | FDA, EMA | R/R AML | Monotherapy | III | 34 | 9.3 | |
IDH1-mut | Ivosidenib | FDA FDA | ND AML in unfit R/R AML | Monotherapy Monotherapy | I I | 42 (ND) 34 (R/R) | 12.6 (ND) 8.8 (R/R) |
IDH2-mut | Enasidenib | FDA | R/R AML | Monotherapy | I/II | 20 | 8.8 |
CD33+ ELN fav/int | Gemtuzumab ozogamicin | FDA, EMA FDA FDA | ND AML R/R AML ND AML in unfit | With 7+3 (DNR) With 7+3 (DNR) Monotherapy | III III III | 81 81 / | 27.5 27.5 4.9 |
t(AML), AML-MRC | CPX-351 | FDA, EMA | ND AML | Monotherapy | III | 48 | 9.6 |
All | Venetoclax | FDA, EMA | ND AML in unfit | With HMA Or LDAC | III | 66 (HMA) 48 (LDAC) | 14.6 (HMA) 8.4 (LDAC) |
All | Glasdegib | FDA, EMA | ND AML in unfit | With LDAC | II | 17 | 8.8 |
Factors | Favor IC | Comment | Favor HMA+VEN | Comment | Favor HMA Alone | Comment |
---|---|---|---|---|---|---|
Age | <70 years | Globally inferior results above 70 years | >85 years | Toxicity concerns | ||
WBC | Leukocytosis | Rapid disease control | Leukopenia | Poor disease control in proliferative disease | ||
Blasts | BM blast <30%, few circulating blasts | Favorable outcomes in oligoblastic AML; circulating blasts associated with lower response rate | ||||
Genes | ||||||
NPM1 | Mutated | Globally good results, although inferior in older adults | Mutated | High response rate, long duration of response in most reports | Wild type | Short duration of response in NPM1-mutated AML |
IDH2 | Mutated | High response rate in most reports, long duration of response | ||||
IDH1 | Mutated | High response rate in some reports | ||||
FLT3-ITD | Mutated | Improved results with IC + FLT3 inhibitors | Wild type | Poor response and short survival with FLT3 aberrations | ||
TP53 | Wild type | Low CR rates and poor survival in TP53-mutated cases | Mutated | Relatively high response rate (around 50%), but short duration of response | Mutated | |
Secondary AML-like gene | Wild type | Poor outcomes if mutated; probable better results with CPX-351 | ||||
Signaling genes | Mutated | Promising role of GO | Wild type | Frequent association of these mutations with relapse and treatment resistance | Wild type | |
Cytogenetics | Favorable | Very high CR rate, prolonged OS | Poor | Relatively high response rate | Poor | |
Phenotype | Non-monocytic | Treatment resistance, lower response rates in monocytic AML | ||||
AML type | De novo | However, better results with CPX-351 in secondary AML | Secondary AML | No significant impact on response | Secondary AML |
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Urbino, I.; Secreto, C.; Olivi, M.; Apolito, V.; D’Ardia, S.; Frairia, C.; Giai, V.; Aydin, S.; Freilone, R.; Dellacasa, C.; et al. Evolving Therapeutic Approaches for Older Patients with Acute Myeloid Leukemia in 2021. Cancers 2021, 13, 5075. https://doi.org/10.3390/cancers13205075
Urbino I, Secreto C, Olivi M, Apolito V, D’Ardia S, Frairia C, Giai V, Aydin S, Freilone R, Dellacasa C, et al. Evolving Therapeutic Approaches for Older Patients with Acute Myeloid Leukemia in 2021. Cancers. 2021; 13(20):5075. https://doi.org/10.3390/cancers13205075
Chicago/Turabian StyleUrbino, Irene, Carolina Secreto, Matteo Olivi, Vincenzo Apolito, Stefano D’Ardia, Chiara Frairia, Valentina Giai, Semra Aydin, Roberto Freilone, Chiara Dellacasa, and et al. 2021. "Evolving Therapeutic Approaches for Older Patients with Acute Myeloid Leukemia in 2021" Cancers 13, no. 20: 5075. https://doi.org/10.3390/cancers13205075
APA StyleUrbino, I., Secreto, C., Olivi, M., Apolito, V., D’Ardia, S., Frairia, C., Giai, V., Aydin, S., Freilone, R., Dellacasa, C., Giaccone, L., Ferrero, D., Audisio, E., Busca, A., & Cerrano, M. (2021). Evolving Therapeutic Approaches for Older Patients with Acute Myeloid Leukemia in 2021. Cancers, 13(20), 5075. https://doi.org/10.3390/cancers13205075