A New Histology-Based Prognostic Index for Acute Lymphoblastic Leukemia (Urayasu Classification for ALL) and a Review of the Literature on Drug Resistance Induced by Functional Proteins in Hematologic Malignancies

1. Introduction

At least eight possible mechanisms underlying treatment resistance in hematopoietic malignancies such as acute lymphoblastic leukemia (ALL) have been reported. 1) enhanced activities of drug efflux mechanisms: extracellular release of many anticancer drugs reduces the intracellular drug concentrations, leading to treatment resistance; 2) suppressed activities of drug influx mechanisms; 3) enhanced activities of drug detoxification pathways; 4) influence of the tumor microenvironment; 5) presence of cancer stem cells; 6) changes in signaling pathways within the tumor cells: e.g., mutations of specific proteins to which molecular-targeted drugs bind; 7) genetic characteristics of the tumor prior to treatment (initial resistance) and genetic mutations acquired after the start of treatment (acquired resistance); and 8) epigenetic changes. Items 5) to 8) have been extensively studied in the medical field. However, items 1) to 4) (references 1-3) have been primarily studied only in the pharmaceutical field, and there have been very few clinical studies linking these factors to clinical treatment resistance. Furthermore, no comprehensive evaluation of these topics has been published in the literature. We previously conducted a comprehensive evaluation of the patient survival time after treatment as an indicator of treatment resistance; our results are summarized in references 2-4.

The efficacy of cancer chemotherapy is determined by the tumor expression profiles of cancer-related genes and functional proteins that play important roles in resistance to anticancer drugs. Factors inducing resistance to anticancer therapy could exist even before the start of treatment (endogenous drug resistance) or emerge after the start of treatment (acquired drug resistance). Drug resistance is the main cause of treatment resistance and disease recurrence in most malignancies. Herein, we provide a guideline for developing more effective cancer treatments in an attempt to obtain better treatment outcomes based on a better understanding of the mechanisms of drug resistance [1].

We have previously proposed prognostic classifications - the Urayasu Prognostic Classification for large B-cell lymphoma (LBCL) [2], aggressive T-cell lymphoma (TCL) [3], and acute myeloid leukemia (AML) [4] - based on the expression patterns of multiple treatment resistance factors present in the tumor cells even prior to the start of treatment (endogenous drug resistance). In patients with newly diagnosed ALL, presence of the Philadelphia chromosome (Ph) indicates a poor prognosis. In patients who test negative for Ph, the prognosis is related to age over 35 years, an elevated white blood cell count (over 30,000/μL for B-ALL and over 100,000/μL for T-ALL), and complete remission within 4 weeks [5]. Herein, we propose the Urayasu classification for ALL based on the expression patterns of multiple resistance (endogenous) factors prior to the start of treatment. For reference, we have briefly outlined the proposed mechanisms of drug resistance in ALL [5].

Regarding ALL, we have proposed a new Urayasu classification based on the combination of multiple (endogenous) protein factors that appear before treatment. We provide a brief explanation of the mechanisms of drug resistance with reference to a review (1). Through an extensive review of the literature, we identified the following four mechanisms as being the most significant in relation to drug resistance in hematopoietic malignancies, namely, 1) enhanced activities of drug efflux mechanisms; extracellular release of the drug results in reduced intracellular concentrations of the anticancer drugs, leading to drug resistance; 2) suppressed activities of drug influx mechanisms; 3) enhanced activities of drug detoxification pathways; and 4) influence of the tumor microenvironment.

1-1. Angiogenesis and vascular hyperplasia due to extracellular release of microenvironmental factors and escape from the immunosurveillance mechanism

1-1-1. Non-immune microenvironmental factors: Factors that facilitate overcoming stress conditions such as hypoxia and hypoglycemia in the tumor microenvironment.

1-1-1-1. Glucose-regulated protein 94 (GRP94) [6,7]

GRP94 exists mainly in the endoplasmic reticulum and/or mitochondria. It is secreted outside the cell and regulates apoptosis, inflammation, and angiogenesis [6]. Upregulation of GRP94 has been observed in various cancers, including multiple myeloma, suggesting the clinical significance of developing treatment agents that can selectively target GRP94 [7].

1-1-1-2. Glucose-regulated protein 78 (GRP78) [8,9,10]

GRP78 is mainly expressed in the endoplasmic reticulum. The expression of GRP78 is known to be associated with cancer and is a potential therapeutic target as well [8]. Expression of this protein has been identified in pediatric patients with high-risk B-cell acute lymphoblastic leukemia (B-ALL) [9]. It has been reported that combined GRP78, CAR-T cell + dasatinib therapy can substantially enhance its effector function [10].

1-1-1-3. Transforming growth factor β1 (TGFβ1) [11,12]

TGFβ1 is involved in cell growth and differentiation, apoptosis, angiogenesis, and cellular immunity. In the early stages of cancer development, it inhibits cell transformation and prevents progression of cancer, whereas in the later stages, it promotes tumor progression through inducing mesenchymal transition, triggering angiogenesis, and inducing immunosuppression [11]. Failure of regulation of the TGFβ1 pathway has been reported in many hematological malignancies, including ALL and malignant lymphoma [12].

1-1-1-4. Tumor necrosis factor α1 (TNFα1) [13]

TNFα is involved in the progression and relapse of AML and is associated with decreased patient survival [13]. We have previously reported that fibrosis caused by TGF-β1 and TNFα1 produced by hematologic malignancies is associated with a poor prognosis [14]. Soluble TNF initiates TNFR1 signaling, but not TNFR2 signaling, despite receptor binding, except as the second messenger [15]. TNFR1 is expressed in AML and promotes proliferation of the tumor cells [16]. TNFR1 and caspase10 expressed in ALL induce cell death [17].

1-1-2. Immune microenvironmental factors (3 types)

1-1-2-1 Programmed cell death-1 (PD-1) (CD279) [18]

1-1-2-2 Programmed cell death–ligand 1(PD-L1, CD274) [19]

In ALL, PD-L1 expression is known to be associated with a poor prognosis.

1-1-2-3 Programmed cell death–ligand 2 (PD-L2, CD273) [20]

In AML, tumor cell surface expression of PD-1/PD-L1,2 is clinically significant, and these patients benefit from immune checkpoint inhibitor therapy.

1-2. Decreased drug uptake activity

Equilibrative nucleoside transporter 1 (ENT1) increases drug uptake [21].

In patients with T-ALL, a decrease in tumor ENT1 expression could decrease cytarabine influx, resulting in treatment resistance.

1-3. Enhanced drug elimination activity

1-3-1. Multidrug resistance 1 (MDR1) [22]: MDR1 is considered as a useful molecular marker of the prognosis in ALL patients.

1-3-2. Multidrug resistance-associated protein 1 (MRP1) [23]: Tumor MRP1 expression has been reported as having a significant effect on the survival in patients with ALL.

1-3-3. Multidrug resistance-associated protein 4 (MRP4) [24]: In patients with ALL, MRP4 could become established as a promising novel therapeutic target to develop agents that can inhibit tumor growth and induce apoptosis.

1-4. Changes in drug metabolic activity

1-4-1. Cytochrome P450 3A4 (CYP3A4) [25]:

CYP3A4 has an important role in the metabolisms of many drugs used in ALL therapy.

1-4-2. CYP2B6 [26]: CYP2B6 variants are significantly associated with the risk level in acute leukemia patients.

1-4-3. Aldo-keto reductase family 1 member C3 (AKR1C3) [27]: In AML and T-cell acute lymphoblastic leukemia (T-ALL), AKR1C3 expression in the tumor cell cytoplasm degrades doxorubicin hydrochloride, resulting in treatment resistance.

1-4-4. AKR1B1 [28]: AKR1B1 induces tumor cell proliferation in the later stages of AML. AKR1B1 is very similar in structure to AR1B10, and the two may competitively inhibit each other’s actions.

1-4-5. AKR1B10 [29]: Intracellular concentrations of daunomycin are decreased mainly by the presence of AKR1B1 and AKR1C3 in the tumor cells; these two proteins also decrease the intracellular concentrations of idarubicin, but only by about one-fifth. AKR1B10 decreases the intracellular concentration of not only daunomycin, but also of idarubicin. It catalyzes reduction in the carbonyl groups of daunomycin and idarubicin in the cytoplasm, converting the drugs into water-soluble inactive alcohols. Like AKR1C3, it is also involved in resistance to cisplatin and is considered as playing a central role in resistance to cyclophosphamide. On the other hand, AKR1C3 is known to be associated with resistance to methotrexate and vincristine. AKR1B10 expression is regulated by genes expressed on chromosome 7q33. Failure of AKR1B10 regulation in the event of missing chromosome 7 results in enhanced function of this protein.

Dasatinib, a Bcr-Abl tyrosine kinase inhibitor, has a moderating effect on AKR1B10 and is expected to be applicable to the treatment of AML because it inhibits the metabolism of daunomycin and idarubicin [30]. Ibrutinib, a tyrosine kinase inhibitor, has a moderating effect on AKR1C3 and is expected to be applicable to the treatment of AML because it inhibits the metabolism of doxorubicin.

1-5. Other functional proteins

1-5-1. Thymidine phosphorylase (TP) [31]: Expression of TP, which is involved in tumor cell resistance to malnutrition and to the tumor cell ability for angiogenesis, infiltration, and metastasis in lymphomas, is associated with a poor patient prognosis due to its anti-apoptotic and angiogenic effects.

1-5-2. P53 [32]: A more rigorous characterization of the resistance profiles and responses to therapy of TP53-mutated ALL is required before its clinical relevance can be established.

1-5-3. MYC [33]: An essential ERG- and c-MYC-dependent transcriptional network involved in the regulation of metabolic and ribosome biogenesis pathways in BCR::ABL1 B-ALL, from which previously unidentified vulnerabilities and therapeutic targets may emerge.

1-5-4. Glutathione sulfate transferase (GST) [34]: GST1 genotyping may be useful for selecting the appropriate chemotherapy regimen for AML [35].

2. Materials and Methods

2.1. Patients and Sample Collection

For this study, we enrolled 19 patients with ALL who had received standard combined Hyper CVAD/MA treatment as the initial remission induction therapy at our hospital between 2015 and 2020. The distribution of the disease types in the patients is shown in Table 1. We performed immunohistochemical (IHC) staining of formalin-fixed paraffin-embedded biopsy specimens obtained from the patients to determine the expressions of 23 proteins that have previously been reported as being treatment-resistance factors; positive and negative staining results were determined by observation under an optical microscope. Biopsies were taken from all patients prior to the start of the induction chemotherapy. In this study, the analysis was limited to investigations performed prior to the start of induction chemotherapy. We then compiled and performed a retrospective analysis of the data. We used variables such as anticancer drug metabolic factors in the analysis model and compared the overall survival periods (OS) of the patients after the initial remission induction therapy by the log-rank test. The MRC UKALL XII/ECOG classification [5] was used as the control. The approval code for this study is U17-0016, and approval for the study by our institution’s Ethics Committee was granted on December 9, 2022.

2.2. Immunohistochemistry

Tissue biopsy specimens obtained from the patients were fixed in formalin and embedded in paraffin to prepare tissue blocks, sectioned, and subjected to IHC staining. The primary antibodies directed against the following major proteins involved in anticancer drug metabolism were as follows: (1) GRP94: Proteintech (Rosemont, IL, USA), clone 1H10B7(monoclonal antibody generated against the N-terminal region of full-length HSP90b1); (2) CYP3A4: Sigma-Aldrich (St. Louis, MO, USA); SAB1400064 (polyclonal antibody generated against CYP3A4); (3) AKR1C3: Proteintech 11194-1-AP (polyclonal antibody generated against AKRC3); (4) MDR1 (P-glycoprotein): Proteintech, 22336-1-AP (polyclonal antibody generated against MDR1); (5) MRP1 (CD9): Proteintech, 60232-1-IG (monoclonal antibody generated against the N-terminal region of full-length MRP1); (6) TGF-beta1: Proteintech, 21898-1-AP (polyclonal antibody generated against TGF-beta); (7) GRP78: Proteintech, 66574-1-IG (monoclonal antibody generated against the N-terminal region of full-length GRP78); (8) glutathione S-transferase-kappa1 (GST): Proteintech, 14535-1-AP (polyclonal antibody generated against GST1); (9) thymidine phosphorylase: Abcam (Cambridge, UK), ab226917 (polyclonal antibody generated against thymidine phosphorylase); (10) MRP4(ABCC4): SANTA CRUZ BIOTECHNOLOGY (Dallas, TX, USA), SC-376262 (monoclonal antibody generated against the N-terminal region of full-length MRP4 [amino acid 1–280]); (11) CYP2B6: LifeSpan BioSciences, Inc. (Seattle, WA, USA), LS-C352084 (polyclonal antibody generated against CYP2B6); (12) TNF1-alpha: Sigma-Aldrich, SAB4502982 (polyclonal antibody generated against TNF1-alpha); (13) PD-1; (14) PD-L1: Proteintech, 66248-1-IG, mouse IgG1 monoclonal antibody. Clone 2B11D11; (15) PD-L2 Proteintech,18251-1-AP16, rabbit IgG polyclonal antibody; (16) P53: Cell Signaling Technology, Inc. (3 Trask Lane Danvers, MA, USA), DO-7 mouse monoclonal antibody #48818; (17) c-MYC: Abcam (Kendall Sq Cambridge, MA, USA), Y69 clone ab32072; (18) ENT-1 (equilibrative nucleoside transporter 1): Proteintech, 1337-1-AP rabbit IgG polyclonal antibody; (19) AKR1B1: Sigma-Aldrich (3050 Spruce Street Saint Louis, MO, USA), rabbit polyclonal antibody HPA052751; and (20) AKR1B10: Sigma-Aldrich (3050 Spruce Street Saint Louis, MO, USA), rabbit monoclonal antibody HPA020280. After the immunostaining, two pathologists definitively determined the results of the IHC staining. The IHC staining was judged as positive when more than 50% of the tumor cells showed positive staining (weakly positive staining was also considered). The concordance rate between the two pathologists for the staining results was about 83%. In case of disagreement between the two pathologists, the final diagnosis was arrived at by consensus.

2.3. Statistical Analysis

To confirm the association between the OS and poor prognostic factors/factors involved in anticancer drug metabolism after the initial Hyper CVAD/MA therapy, survival curves were plotted by the Kaplan–Meier method, and factors significantly associated with the OS were evaluated by the log-rank test. The significance level in the statistical tests was set at 0.05 (two-tailed), and p < 0.05 was considered as being indicative of a statistically significant difference. Statistical analyses were performed using EZR version 2.7-1 software (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [36].

Multiple comparisons were not considered because of the exploratory nature of this study.

2.4 A comprehensive literature review of 33 anti-hematologic malignancy drugs related to five factors of treatment resistance and three factors of treatment enhancement.

2-4-1 The 33 representative anti-hematologic malignancy drugs

2-4-1-1 CHOP: Cyclophosphamide, Doxorubicin, Vincristine, Prednisolone, or Dexamethasone (steroid)

2-4-1-2 Tyrosine kinase inhibitors (TKIs): Dasatinib, Asciminib, Ponatinib

2-4-1-3 IMiDs: Lenalidomide, Pomalidomide

2-4-1-4 Protease inhibitors: Bortezomib, Carfilzomib

2-4-1-5 Bruton's Tyrosine Kinase Inhibitors (BTKIs): Ibrutinib

2-4-1-6 Others: Cisplatin, Methotrexate, Gemcitabine, Cytarabine (Ara-C), Romidepsin, Venetoclax, Monomethyl auristatin E (MMAE) Gilteritinib, Quizartinib, Chridamid, Etoposide Bendamustine, Carfilzomib, Melphalan, Darinaparsin, Pralatrexate, Etoposide, Bendamustine, Azacitidine (AZA), All trans retinoic acid (ATRA), Forodesine, Radiation

2-4-2 Five typical treatment resistance factors expressed in hematologic malignancies: The drug-metabolic enzymes AKR1 (AKR1B10, AKR1C3) and CYP3A4, the efflux pump MRP1, and MDR1

2-4-3 Three representative factors enhancing the treatment efficacy:

Influx pump ENT-1, CYP2B6, and AKR1B1

3. Results

3.1. Kaplan–Meier Survival Curves and Comparisons of Survival Outcomes Among Various Groups by the Log-Rank Test

3.1.1. Overall Survival of ALL Patients with and Without Various Prognostic Factors

As shown in Figure 1, the median OS duration in the 19 patients was 57 months after the initial remission induction therapy, and the 5-year OS rate was 44%. A log-rank test was performed to compare the OS rates in relation to positive/negative tumor cell expressions of various factors involved in anticancer drug metabolism. There were no significant differences in the OS rates among the three patient groups classified according to the ALL MRC UKALL classification or between the groups that did and did not undergo allogeneic stem cell transplantation. However, a significant difference in the relapse rate within one year was seen between these latter two groups of patients.

3.1.2. Overall Survival of ALL Patients with and Without Expression of a Prognostic Factor by the Histological Immunostaining

As shown in Figure 2, a log-rank test was performed to compare the OS rates in the ALL patients classified in accordance with the expression patterns of proteins involved in anticancer drug metabolism. The patients in this group showed significant differences in the patterns of expressions of AKR1B10 and AKR1C3. However, there were no significant differences in the OS rates among the patients with AKR1B1, MRP1, PD-L2 and CYP2B6.

3.1.3. Overall Survival of ALL Patients with and Without Expression of the Two Prognostic Factors by the Histological Immunostaining

As shown in Figure 3, the results of univariate analyses showed that the decrease in OS rates differed significantly among patients showing different patterns of expression of AKR1C3, AKR1B10, AKR1B1 (a competitive inhibitor of AKR1B10), and MRP1, and the Philadelphia chromosome.

The OS rates differed significantly among patients showing different combinations of AKR1C3 and AKR1B10 expressions, AKR1B1 and AKR1B10 expressions, AKR1B10 and CYP2B6 expressions, MRP1 and AKR1C3 expressions, Philadelphia chromosome status and AKR1C3 expression.

3.2. Urayasu Classification for ALL

As shown in Figure 4, we have proposed the Urayasu classification for ALL based on the expression patterns of the important treatment resistance factors (AKR1B1 and AKR1B10), in addition to the status of expression of MRP1. The classification is as follows:

. Group 1 (good prognosis group): AKR1B1(+) AKR1B10 (-), n =1

. Group 2: AKR1B1(-) AKR1B10(-) MRP(-), n=9

. Group 3: AKR1B1(-) AKR1B10(-) MRP(+), n=6

(Group 2 and Group 3 showed a more favorable prognosis than Group 4)

. Group 4 (poor prognosis group): AKR1B1(-) AKR1B10(+), n=3. Significant differences in the OS rates were observed among the four groups. These results are also shown in Table 2.

Table 2 shows the results of immunohistochemical analysis for 23 treatment resistance factors. The median cumulative survival rate and 95% CI of the 19 cases of ALL1 calculated by the Kaplan-Meier method, as well as the results of intergroup comparison (p-value: log rank test) are shown. Poor prognostic factors were evaluated based on differences in the survival time of the patients. Significant (p<0.05) poor prognostic factors are marked with a (#). The outcomes were compared with those in the three groups of patients classified according to the MRC classification, which is the conventional prognostic classification.

3.3. Case Presentation: Urayasu Classification for ALL (Figure 5A, Figure 5B, Figure 5C and Figure 5D)

All figures and tables should be cited in the main text as Figure 1, Table 1, etc.

3.4. Abstract Schema in this Study

Abstract schema of this study is shown in Figure 6.

3-6 Summarization of the review of the literature (references 1-3) plus findings of this study (Figure 7)

This is a summary of the Urayasu classification based on treatment resistance

factors for four aggressive hematopoietic tumors.

Figure 7. The review of the literature (references 1-3) plus findings of this study. Regarding the relationship between treatment resistance factors in the Urayasu classification and this study's LBCL [1], TCL [2], AML [3], and ALL [this study] This is a summary of the Urayasu classification based on treatment resistance factors for four aggressive hematopoietic tumors. The prognostic significances of the treatment resistance factors in the Urayasu classification for LBCL (2), UC for TCL (3), UC for AML (4), and UC for ALL (current study) are summarized in a Venn diagram for each disease. There were no factors that were common to all four diseases. Common factors in the first three diseases were the growth factor P53 (LBCL 36%, TCL 13%, AML 9%), which was more prevalent in LBCL, the efflux pump MRP1 (LBCL 25%, AML 9%, ALL 46%), and AKR1C3, the enzyme that metabolizes doxorubicin, methotrexate, vincristine, and dasatinib (LBCL, TCL, ALL). Common to LBCL, TCL were the microenvironmental adaptation factor, ER stress protein GRP94, and common to AML and ALL was AKR1B10, which metabolizes idarubicin, daunorubicin, cyclophosphamide, and cisplatin. Dasatinib inhibits AKR1B10. The tyrosine kinase inhibitor ibrutinib inhibits AKR1C3, thereby inhibiting doxorubicin metabolism and showing promise for potential therapeutic application. Five survival activators (PD-1, PD-L1, TP, GRP78, and GRP94), which are microenvironmental factors, are also important in TCL. CYP3A4, which detoxifies doxorubicin, and the doxorubicin efflux pump MDR1 are important in LBCL. The HO efflux pump MRP1 is more frequently expressed in ALL, AML, and LBCL than in TCL, which is dependent on microenvironmental adaptation factors. P53 is involved in many diseases other than ALL. However, the poor prognosis of TCL is largely due to p53 expression. p53 is an important tumor suppressor, and loss of p53 function due to mutations or other factors leads to cancer development. p53 mutations occur in more than 50% of human cancers. However, currently, no drugs have been approved for the clinical treatment of cancers expressing mutant p53. LBCL, AML, and TCL are most frequently positive for AKR1C3, an enzyme that inactivates doxorubicin, cyclophosphamide, and vincristine (AML 17/35: 46%, TCL 6/16: 38%, LBCL 26/42: 62%). Based on these findings, while R-CHOP and Pola-RCHP regimens are standard therapies, many LBCL tumors express AKR1C3, which may contribute to treatment resistance. AML patients with P53 (3/35 9%) or MRP1 (1/35 3%) mutations show a poor prognosis. Although the incidence rate is low (12%), even with combined treatment, MRP1 inhibitors have been developed. In addition to significant microenvironmental factors in TCL, AML cells can express AKR1B10, a drug that metabolizes the anthracycline anticancer drug idarubicin, which can result in treatment resistance. AKR1B1 shares 70.6% amino acid sequence identity with AKR1B10 and shares highly similar structure and substrate specificity. Many compounds that inhibit AKR1B10 also competitively inhibit AKR1B1 to a similar degree, reducing AKR1B10 activity and reversing treatment resistance. The Abl tyrosine kinase inhibitor dasatinib inhibits AKR1B10, inhibiting daunomycin and idarubicin metabolism, offering promise for potential therapeutic application. AKR1B10 is believed to be the primary pathway underlying cisplatin and cyclophosphamide resistance.29 In fact, caution is required in the case of AKR1B10-positive ALL, in which high-dose cyclophosphamide therapy used as a conditioning regimen for allogeneic hematopoietic stem cell transplantation may show reduced efficacy. Furthermore, AKR1B10 is regulated by a gene on chromosome 7q33. Loss of chromosome 7 may result in the activation of AKR1B10. Meanwhile, TCL has a superior ability to adapt to the microenvironment, and LBCL is also thought to have a superior ability to detoxify anticancer drugs.

Figure 7. The review of the literature (references 1-3) plus findings of this study. Regarding the relationship between treatment resistance factors in the Urayasu classification and this study's LBCL [1], TCL [2], AML [3], and ALL [this study] This is a summary of the Urayasu classification based on treatment resistance factors for four aggressive hematopoietic tumors. The prognostic significances of the treatment resistance factors in the Urayasu classification for LBCL (2), UC for TCL (3), UC for AML (4), and UC for ALL (current study) are summarized in a Venn diagram for each disease. There were no factors that were common to all four diseases. Common factors in the first three diseases were the growth factor P53 (LBCL 36%, TCL 13%, AML 9%), which was more prevalent in LBCL, the efflux pump MRP1 (LBCL 25%, AML 9%, ALL 46%), and AKR1C3, the enzyme that metabolizes doxorubicin, methotrexate, vincristine, and dasatinib (LBCL, TCL, ALL). Common to LBCL, TCL were the microenvironmental adaptation factor, ER stress protein GRP94, and common to AML and ALL was AKR1B10, which metabolizes idarubicin, daunorubicin, cyclophosphamide, and cisplatin. Dasatinib inhibits AKR1B10. The tyrosine kinase inhibitor ibrutinib inhibits AKR1C3, thereby inhibiting doxorubicin metabolism and showing promise for potential therapeutic application. Five survival activators (PD-1, PD-L1, TP, GRP78, and GRP94), which are microenvironmental factors, are also important in TCL. CYP3A4, which detoxifies doxorubicin, and the doxorubicin efflux pump MDR1 are important in LBCL. The HO efflux pump MRP1 is more frequently expressed in ALL, AML, and LBCL than in TCL, which is dependent on microenvironmental adaptation factors. P53 is involved in many diseases other than ALL. However, the poor prognosis of TCL is largely due to p53 expression. p53 is an important tumor suppressor, and loss of p53 function due to mutations or other factors leads to cancer development. p53 mutations occur in more than 50% of human cancers. However, currently, no drugs have been approved for the clinical treatment of cancers expressing mutant p53. LBCL, AML, and TCL are most frequently positive for AKR1C3, an enzyme that inactivates doxorubicin, cyclophosphamide, and vincristine (AML 17/35: 46%, TCL 6/16: 38%, LBCL 26/42: 62%). Based on these findings, while R-CHOP and Pola-RCHP regimens are standard therapies, many LBCL tumors express AKR1C3, which may contribute to treatment resistance. AML patients with P53 (3/35 9%) or MRP1 (1/35 3%) mutations show a poor prognosis. Although the incidence rate is low (12%), even with combined treatment, MRP1 inhibitors have been developed. In addition to significant microenvironmental factors in TCL, AML cells can express AKR1B10, a drug that metabolizes the anthracycline anticancer drug idarubicin, which can result in treatment resistance. AKR1B1 shares 70.6% amino acid sequence identity with AKR1B10 and shares highly similar structure and substrate specificity. Many compounds that inhibit AKR1B10 also competitively inhibit AKR1B1 to a similar degree, reducing AKR1B10 activity and reversing treatment resistance. The Abl tyrosine kinase inhibitor dasatinib inhibits AKR1B10, inhibiting daunomycin and idarubicin metabolism, offering promise for potential therapeutic application. AKR1B10 is believed to be the primary pathway underlying cisplatin and cyclophosphamide resistance.29 In fact, caution is required in the case of AKR1B10-positive ALL, in which high-dose cyclophosphamide therapy used as a conditioning regimen for allogeneic hematopoietic stem cell transplantation may show reduced efficacy. Furthermore, AKR1B10 is regulated by a gene on chromosome 7q33. Loss of chromosome 7 may result in the activation of AKR1B10. Meanwhile, TCL has a superior ability to adapt to the microenvironment, and LBCL is also thought to have a superior ability to detoxify anticancer drugs.

3.7. Review of the Literature on the Drug-Resistance Mechanisms in Hematopoietic Malignancies

Table 3 shows the results of the literature on the drug-resistance mechanisms in hematopoietic malignancies. We conducted a literature search to examine the relationship between each functional protein (row) that affects the concentration of each anti-hematologic malignancy agent (column) in the tumor cells.

As shown in Figure 8, a summary of the prognostic factors associated with treatment resistance in LBCL [1], TCL [2], AML [3], and ALL (current study) in an easy-to-understand Venn diagram with four circles in the literature.

4. Discussion

Among the mechanisms of resistance to anticancer drugs in hematopoietic malignancies, those involving a decrease in intracellular anticancer drug concentrations appear to be especially important, although there have been no comprehensive investigations of their association with clinical treatment resistance. In this study, we focused on the following mechanisms that can reduce the intracellular drug concentrations of anticancer drugs: 1) enhanced activities of anticancer drug efflux mechanisms; 2) suppressed activities of drug influx mechanisms; 3) enhanced activities of drug detoxification pathways; and 4) influence of the tumor microenvironment. In the following sections 4-1 to 4-3, we describe our study conducted in 19 cases of ALL (42 cases of LBCL in Reference 2, 16 cases of TCL in Reference 3, and 35 cases of AML in Reference 4) that underwent initial induction therapy at our hospital; we performed immunohistochemical analysis for 23 resistance-associated proteins in tumor specimens obtained from these patients. We retrospectively analyzed the correlations between the OS duration/rate of the patients and results of the immunohistochemical analyses by the Kaplan-Meier method. In section 4-4, we summarize the results of a literature search conducted by us to review the proposed drug resistance mechanisms in hematopoietic malignancies. Based on the clinical study and review of the literature, we have comprehensively demonstrated associations between the mechanisms of treatment resistance involving a decrease in intracellular anticancer drug concentrations and clinical treatment resistance.

4-1. Discussion based on the Urayasu classification for ALL proposed in this study (Table 1 and Table 2, Figure 1, Figure 2, Figure 3 and Figure 4)

In general, immunohistochemistry is considered as being an extremely useful tool, as it allows for determinations of positive/negative results based on the observation of tumor cells under an optical microscope. Positivity is defined as positive staining, including weakly positive staining, in ≥50% of tumor cells. In this study of ALL patients, we performed immunohistochemical analyses in tumor specimens obtained from 19 cases of ALL for 23 treatment resistance factors (6-34) determined as being clinically important based on a review of the literature (Table 1). In this cohort of ALL patients, there were no significant differences in the OS among patient groups classified according to the MRC UK ALLXIIECOG E2993 classification (Figure 1B–D). However, as shown in Figure 4A,B, the Urayasu classification for ALL allowed reliable prognostic stratification of the patients, with significant differences in the OS observed among the patients classified into the four groups according to this classification. Therefore, chemotherapy resistance factors are more specific for prognostic classification of ALL. We propose the Urayasu classification (UC) for ALL as a useful classification for predicting the outcomes after Hyper CVAD/MA therapy in newly diagnosed ALL patients (Figure 4A,B). The UC for ALL (groups 1 to 4) is shown below. In this study, the overall 5-year OS rate of the patients (n=19) was approximately 43%. The 5-year OS rate of patients (n=1521) classified by the MRC UKALLXIIECOG E2993 classification was approximately 38.5% (5). The 5-year OS rate was 100% in UC Group 1 (favorable prognosis group; AKR1B10(-) AKR1B1(+); n=1) versus 57% in the MRC UKALLXIIECOG E2993- Ph-negative Standard group. The 5- year OS was 48% in UC Group 2 (intermediate-1 prognosis group; AKR1B10(-) AKR1B1(-) MRP1(-); n=9) versus 35% in the MRC UKALLXIIECOG E2993-Ph-negative High group (n=594). The 5-year OS was 20% in UC Group 3 (intermediate-2; AKR1B10(-) AKR1B1(-) MRP1(+); n=6). The 5-year OS rate was 0% in Group 4 (poor prognosis group; AKR1B10(+) AKR1B1(-); n=3). As seen from the above, although the number of cases was small, the UC for ALL allows more reliable prognostic classification of patients into good- and poor-prognosis groups as compared with the UKALLXIIECOG E2993 classification (Figure 1B,C). We believe that this classification could contribute to stratification of treatment and promote development of treatment methods based on the mechanisms of treatment resistance.

4-2. Discussion based on the study of ALL cases (Figure 5)

In Case 1 (Figure 5A), the blast cells at the time of diagnosis showed positive IHC staining for the factors known to enhance the therapeutic efficacy AKR1B1 and ENT1. The therapeutic inhibitors were negative for AKR1B10, AKR1C3, and CYP3A4, but positive staining for MRP1. The tumor was negative for AKR1B10 and AKR1C3, and the staining result was negative for enzyme AKR1. The patient was classified into Group 1 of the UC for ALL. See Table 3, Figure 6 and Figure 8. The tumor was positive for the metabolic enzyme AKR1B1, suppressed for AKR1B10, and negative for AKR1B10 and AKR1C3. Thus, dasatinib, cyclophosphamide, doxorubicin, vincristine, and methotrexate were expected to be effective, and proved to be effective even in the presence of expression of the efflux pump MRP1. Cytarabine and methotrexate may have been highly effective as the positive tumor cell expression of the influx pump ENT1 would have allowed large amounts of these drugs to be taken up by the tumor cells. It is believed that complete remission was achieved quickly after Hyper CVAD/MA therapy and maintained for a long period of time based on the above pattern of expression of the prognostic factors.

In Case 2 shown in Figure 5B, the blast cells at diagnosis were AKR1B1(-), AKR1B10(-), and MRP1(-), so that the patient was classified into Group 2 of the UC for ALL. Please refer to Table 3, Figure 6 and Figure 8. In regard to the expressions of drug-metabolic enzymes, the tumor was AKR1B10(-), but showed weakly positive staining for AKR1C3. The weak AKR1C3 positivity resulted in some degree of metabolism of dasatinib, doxorubicin, vincristine, and methotrexate. However, cyclophosphamide was highly effective because of the AKR1B10(-). Furthermore, cytarabine and methotrexate may have been highly effective as the positive tumor ENT1 expression would have allowed large amounts of these drugs to be taken up by the tumor cells. It is believed that the patient achieved complete remission after Hyper CVAD/MA therapy based on the above pattern of expression of the prognostic factors. However, because of the weak tumor cell positivity for AKR1C3, there was residual disease and early recurrence occurred approximately 6 months later. Radiation and cyclophosphamide, which are conditioning treatments for allogeneic hematopoietic stem cell transplantation after relapse, were effective because the tumor cells were negative for AKR1B10, which is a major radiation therapy resistance factor and the main cyclophosphamide-metabolizing enzyme. This is thought to be why the patient achieved long-term complete remission after allogeneic hematopoietic stem cell transplantation.

The blast cells at diagnosis in Case 3 shown in Figure 5C were AKR1B1(-), AKR1B10(-), and MRP1(+), so that the patient was classified into Group 3 of the UC for ALL. See Table 3. In regard to the expressions of drug-metabolic enzymes, the tumor cells were AKR1B10(-) and AKR1C3(-), so that the treatment was effective. However, due to the MRP1 positivity, dasatinib, doxorubicin, vincristine, and methotrexate were excreted from the tumor cells, which may have resulted in unsustained complete remission. The patient eventually developed disease relapse and received ponatinib plus VP therapy, which was not very effective. It was presumed that none of the drugs other than prednisolone was effective, because they were excreted from the tumor cells by the MRP1 pump, which led to disease progression and eventual death of the patient.

In Case 4 shown in Figure 5D, the blast cells at diagnosis were AKR1B1(-) and AKR1B10(+), so that the patient was classified into Group 4 of the UC for ALL. Please refer to Table 3. In regard to the expressions of drug-metabolic enzymes, the tumor cells were AKR1B10(+) and AKR1C3(+), and were therefore resistant to treatment. Metabolism of dasatinib, cyclophosphamide, doxorubicin, vincristine, and methotrexate was accelerated, and since the cells were also MRP1(+), these anticancer drugs were also excreted from the tumor cells, resulting in resistance to treatment. However, cytarabine was effective as the positivity of the tumor cells for ENT1(+) would have allowed large amounts of the drug to be taken up by the cells, and unsustained complete remission was achieved at one point. However, thereafter, the patient showed early disease relapse and the tumor burden was large, so that complete remission was not achieved even with ponatinib + Hyper CVAD/MA therapy, and the disease progressed, resulting in the death of the patient.

4-3. Discussion based on the study of ALL cases (Figure 6)

Figure 6 discusses the effects of the expression patterns of the enzymes in Groups 1-4 of the UC for ALL described in Figure 4. For LBCL, TCL, and ALL, for which CHOP-like regimens are often selected as the initial regimens, the expression statuses of the HO (hydroxyl doxorubicin, oncovin)-metabolizing enzymes AKR1C3 and AKR1B10 are important. These enzymes are also involved in methotrexate metabolism. AKR1B10 plays a central role in cyclophosphamide metabolism. Furthermore, it is reasonable to assume that dasatinib has an AKR1B10-inhibitory effect in Ph-positive ALL cases. All drugs but cyclophosphamide are excreted by the MRP1 pump. Cytarabine enters the cells via the ENT-1 pump.

4-4 Discussion based on the findings of both our previous studies [1,2,3] and this study (Figure 7)

Figure 7 summarizes the prognostic factors for aggressive hematopoietic tumors determined from previous studies (references 1-3) and this present study of ALL. Treatment resistance factors include microenvironmental factors and factors reducing the tumor cell concentrations of anticancer drugs. (1) LBCL: According to reference 1, the median survival time (OS) was 64 months, and microenvironmental factors included a high expression rate of GRP94 (90%) of factors reducing the intracellular concentrations of anticancer drugs (CYP3A4 7%, AKR1C3 62%, AKR1B10 62%, MDR1 40%, MRP1 26%), and of P53 (36%), all of which are treatment resistance factors. (2) TCL: According to reference 2, the OS was 34 months, representing a poor prognosis. Treatment resistance factors were mainly factors related to the tumor microenvironment (GRP94 63%, GRP78 63%, PD-1 31%, PD-L1 24%, TP 19%). Therefore, even if complete remission is achieved with standard chemotherapy alone, recurrence is likely to develop even if there is minimal residual disease containing cells expressing GRP94. In cases where complete remission was not achieved, AKR1C3 (63%) and P53 (13%) could have been involved. (3) AML: According to reference 3, the 5-year OS was 73%. Treatment resistance factors included factors reducing the tumor cell concentrations of anticancer drugs such as P53 (9%), MRP1 (9%), and AKR1B10 (49%). The microenvironment factor GRP94 was expressed at a high frequency of 94%, but it did not have any significant influence on the treatment efficacy. (4) ALL: According to the results of this study, the OS duration was 57 months, indicative of a poor prognosis. Because Hyper CVAD treatment is powerful, many patients achieved temporary complete remission, but many were also at a high risk of relapse. The main treatment resistance factors were factors that reduced the tumor cell concentrations of anticancer drugs, including AKR1B10 %, MRP1 46%, and AKR1C3 37%, which increased the risk of relapse. AKR1B1 is a factor that antagonizes AKR1B10 that enhances the efficacy of chemotherapy and is important in ALL (5%). (5) Common treatment resistance factors: There are no common treatment resistance factors across the four diseases. However, the two common treatment resistance factors across the first three diseases are P53 (LBCL 36%, TCL 19%, AML 9%) and MRP1 (ALL 46%, LBCL 26%, AML 9%).

To gain a deeper understanding of resistance to chemotherapy, molecular-targeted drugs and other therapeutic agents shown in Figure 7, we conducted a comprehensive literature review and the findings are summarized in Table 3. Table 3 also summarizes the relationship between 34 therapeutic agents, primarily used to treat hematological malignancies, and eight functional proteins that metabolize each therapeutic agent (chemotherapy inactivators shown in Figure 7). For those reported in the literature, "Decrease," in Table 3 indicating intracellular detoxification of the therapeutic agent, is listed with the supporting reference number in parentheses. Blank spaces indicate no literature reports. Based on these results, in Figure 8, the 34 therapeutic agents are aggregated into 24 categories, and the eight functional proteins are aggregated into four types. The frequency of each of these four types in the tumor cells is listed. From this, we were able to classify the tumors into nine categories, as shown in Figure 8. The four major metabolic pathways shown in Figure 8 are pathways (1) to (4) described below. (1) AKR1 enzymes (represented by AKR1B10 and AKR1C3): generally expressed in hematologic tumors, particularly in LBCL and TCL, where they metabolize CHO (cyclophosphamide, doxorubicin or daunomycin or idarubicin, vincristine), methotrexate, gemcitabine, etc. AKR1C3 is important in LBCL (reference 2) and TCL (reference 3). (2) CYP3A4 enzymes: their expression rate is low at around 10%, and they are not expressed in TCL or ALL, in particular. They metabolize CHOP, venetoclax, MMAE, cytarabine, etc. (3) Efflux pumps (MRP1, MDR1): highly expressed in ALL and LBCL. Involved in the efficacy of most drugs (21 types), they expel etoposide, bendamustine, etc., from the tumor cells. This reduces their concentrations within the tumor cells, weakening their efficacy and causing tumor persistence. On the other hand, the (4) influx pump (ENT1) promotes uptake of methotrexate, gemcitabine, cytarabine, etoposide, bendamustine, etc., by the tumor cells. This influx increases the concentrations of the drugs within the tumor cells, enhancing their efficacy. The patterns of expression of the four pathways can be classified into nine categories as shown in Figure 8. These categories are important and will provide extremely valuable information for future treatment selection.

Based on the results of the IHC analysis of tumor specimens for at least three treatment resistance proteins (AKR1B10, AKR1B1 and MRP1), patients with ALL can be classified into four prognostic groups, the so-called UC for ALL. The classification may be a simple, useful, and innovative classification that also explains the mechanisms of resistance to treatment in each group. If validated in a larger number of patients, the UC for ALL will enable targeted treatment using selected inhibitors directed against the offending protein in each patient. We propose to conduct further validation by analyzing a larger number of cases in the future.

4-5.Discussion based on the Review of the literature on the drug-resistance mechanisms in hematopoietic malignancies

Table 3 and Figure 8 shows the results of the literature on the drug-resistance mechanisms in hematopoietic malignancies

The therapeutic agents for hematological malignancies (AML, ALL, TCL, LB, etc.) have been converted from Table 3 to a Venn diagram in Figure 8, summarizing the mechanisms of therapeutic resistance. The therapeutic resistance mechanisms involve at least three enzymes (AKR1B10, AKR1C3, and CYP3A4), one inflow pump that enhances the therapeutic efficacy (ENT1), and two efflux pumps. LB stands for large B-cell lymphoma. Furthermore, the incidence of each tumor is shown as a percentage based on references 1-3 as well as this study. Stars (★) indicate tumors with significant differences in the tumor survival time for each factor. Many anticancer drugs and molecular- targeted drugs are taken up into tumor cells via concentration gradient inflow pumps. 1 However, MTX, gemcitabine, cytarabine (Ara-C), romidepsin, etoposide, bendamustine, azacitidine (AZA), and forodesine are transported via the inflow pump ENT1. In cells that do not express ENT1, the efficacy of these agents is diminished. However, ENT1 is expressed in 100% of TCLs. 2) Many anticancer drugs and molecular- targeted drugs are metabolized by three enzymes (AKR1B10, AKR1C3, and CYP3A4) and two efflux pumps in tumor cells, reducing their effectiveness. 3) The CYP3A4 expression rate is 0% in TCLs and ALLs. CYP3A4 expression is also rare overall. 4) Furthermore, efflux pump expression is rare in TCL, so that AKR1B10 and AKR1C3 are the major factors involved in treatment resistance in TCL. In addition to AKR1, the microenvironmental factors shown in Figure 7 are also important. 5) In LBCL (LB), ALL, and AML, the AKR1 enzymes and efflux pumps are often involved in treatment resistance.

5. Conclusions

In summary, based on the results of IHC analysis of tumor specimens for at least three treatment resistance proteins (AKR1B10, AKR1B1 and MRP1), patients with ALL can be classified into four prognostic groups, the so-called UC for ALL, as follows: Group 1 (favorable prognosis group); Group 2 (intermediate-1 prognosis group); Group 3 (intermediate-2 prognosis group) and Group 4 (poor prognosis group). In the future, we propose to use AKR1B10 and MRP1 inhibitors as a stratified treatment approach for ALL based on the UC to obtain improved treatment outcomes in patients with new-onset ALL. Furthermore, we also propose to conduct similar analyses in a larger number of cases, as well as in patients with other hematological malignancies and summarize the results. IHC staining for treatment resistance proteins provides quick results and is simple and inexpensive. Furthermore, it is noteworthy that our UC for ALL classified more than twice as many patients into the poor prognosis group as the MRC UKALLXIIECOG E2993 classification. We would like to conduct further validation of this classification system by analyzing a larger number of cases in the future.

Furthermore, based on the additional literature review, we concluded that the four major drug metabolic pathways in the tumor cells are: 1) AKR1 enzymes (represented by AKR1B10 and AKR1C3): expressed at a particularly high frequency in LBCL and TCL, they metabolize CHO drugs, methotrexate, gemcitabine, etc.; 2) CYP3A4 enzymes: not expressed in TCL or ALL. They metabolize the CHOP drugs, venetoclax, MMAE, cytarabine, etc.; 3) efflux pumps (MRP1, MDR1): highly expressed in ALL and LBCL. They expel anticancer drugs from tumor cells and may promote tumor persistence; 4) influx pump (ENT1): this allows anticancer drugs to enter the tumor cells and may improve the treatment efficacy. Based on the combined effects of these four pathways on the drugs, 33 representative drugs were classified into nine categories, which is an important categorization that is expected to provide extremely valuable information for future treatment selection.