Prom-1 (Cd 133) Overexpression in Adult Acute Lymphoblastic Leukemia Egyptian Patients and Relation to Outcome

INTRODUCTION

CD133, encoded by the PROM1 gene, is a pentaspan transmembrane glycoprotein of great potential value as a pan-cancer target as it is commonly associated with cancer stem cells in multiple different tumor types, including leukemia.Proof-of-principle studies have shown that targeting CD133 can be used to deliver nanoparticles to gastric stem cells,or for chimeric antigen receptor T cell therapy in acute lymphoblastic leukemias (ALL) caused by rearrangements of the Mixed Lineage Leukemia (MLL) gene (Liou, 2019).

Despite vast improvements in treatment for ALL, MLL gene rearrangements (MLLr) still cause very poor prognosis ALLs. The most common MLL rearrangement is the t(4;11) (q21;q23) chromosome translocation that fuses MLL in frame with the AF4 gene producing MLL-AF4 and AF4-MLL fusion protein. MLL-AF4 and other MLL fusion proteins (MLL-FPs) bind to gene targets and cause inappropriate gene activation through multiple transcription elongation and epigenetic mechanisms, including recruitment of the histone H3 lysine-79 (H3K79) methyltransferase DOT1L. In addition to a role in transcription elongation, recent work has shown that H3K79me2/3 has an important role at a subset of enhancers (H3K79me2/3-marked enhancer elements (KEEs)), increasing expression of key gene targets through the maintenance of enhancer–promoter interactions (Pieters, 2019).

According to Tolba et al., study which showed that CD133 expression is an independent prognostic factor in acute leukemia, especially ALL patients and its expression could characterize a group of acute leukemia patients with higher resistance to standard chemotherapy and relapse (Tolba et al., 2013).

 One of the most attractive features of PROM1/CD133 as a potential therapeutic target derives from the recognition that the gene is a direct target of MLL-AF4 regulation, suggesting that in MLLr leukemias PROM1/CD133 expression is tightly linked to the activity of the fusion protein itself. However, the exact details of how this locus is regulated by MLL-AF4 are unclear, and whether and how PROM1 /CD133 contribute to MLLr leukemic growth is unknown. Understanding these mechanisms is likely to be key to the future development of PROM1/CD133-directed therapeutic targeting in these leukemias (Godfrey. 2019).

AIM OF THE WORK

The aim of the present study is to know about PROM-1 (CD 133) overexpression in adult acute lymphoblastic leukemia Egyptian patients and relation to outcome.

PATIENTS AND METHODS

Type of the study: Prospective study. Study setting: The study had been conducted at Ain Shams university hospitals. Internal medicine department, Clinical hematology and stem cell transplantation unit. Study period: Six months. Sample size: 47 adult patients with Acute Lymphocytic Leukemia. Study population: total count of study 47 patients.15 of them will be the control group and 32 of them will be ALL patients.

Inclusion criteria: All Adult Patients with Acute Lymphocytic Leukemia that have low or intermediate risk.

Exclusion criteria: Pediatric patient with Acute lymphocytic leukemia, Patients with other haematological malignancy and 3.patient with solid malignancies

Methods: All patients had been subjected to all of the following at time of recruitment

Full history taking and Clinical examination.

Laboratory investigations including: Complete blood count with differential,, peripheral blood film,ESR, reticulocyte percentage, LDH, uric acid, Kidney and Liver function test, Prothrombine time (PT), Partial thromboplastine time (PTT), Viral screening tests: HBsAg, HBsAb, HCVAb and HIV Ab, Disease-specific labs:-Bone marrow aspiration for morphology, flowcytometry to confirm diagnosis and cytogenetic study and CD133 will be measured by flowcytometry on bone marrow and /or peripheral blood samples at diagnosis to asses patient outcome.

Radiological finding: Chest X-ray, Abdominal U/S +/- abdominal and chest CT, MRI or CT Brain, PETSCAN if needed to detect extra-medullary disease.

Bone marrow aspiration:

Procedure: Obtain consent from the patient or from a parent or guardian, Review patient identifiers to make sure you will perform the procedure on the intended patient, Be sure that the site you plan to use is the correct one, If the posterior iliac crest is the chosen site, patients are generally placed in the lateral decubitus position or the prone posit ion and sterilize the site with the sterile solution, place a sterile drape over the site, and administer local anesthesia, letting it infiltrate the skin, soft tis-sues, and periosteum.

After local anesthesia has taken effect, make an incision through which you can introduce the bone marrow aspiration needle. Some needles used for intraosseous access have a guard in place to keep the needle from passing all the way through the bone. If a guard is present, you should remove it before starting bone marrow aspiration, to ensure adequate depth of penetrat on. Since the ileum is a large bone, the marrow space should be easy to locate, but the angle of entry is also import ant. In general, the needle should be advanced at an angle completely perpendicular to the bony prominence of the iliac crest.

Once the needle passes through the cortex and enters the marrow cavity, it should stay in place without being held. Once the periosteum has been penetrated, use pressure to advance the needle through the cortex and rotate the needle in a semicircular motion, alternating clockwise and counterclockwise movements. If the patient is in the lateral position, you may stabilize the hip with your other hand so that you can get a better feel for the position and depth of the needle. You may use the thumb of this hand to mark the desired site and to prevent accidental repositioning of the needle. You will feel a slight give, after which you will feel that the needle is fixed solidly within the bone. Remove the stylet and aspirate approximately 1 ml of unadulterated bone marrow into a syringe.

An assistant should take the specimen from you and assess it for the presence of bony spicules; sometimes this can be done by merely looking at the f low of the blood in the syringe, but it is more easily achieved by spreading a drop of blood on a slide or dish and allowing it to spread. Bony spicules will appear as irregularities in the otherwise smooth surface of the drop. If the specimen shows spicules, the assistant should use it to make smear slides immediately. If spicules are sparse or are not present, a new sample should be obtained from a slightly different site. At this point, speed is important. Leave the needle in place and fill sequential syringes that have been prepared with heparin or other anticoagulants or preservatives, depending on the requirements for specific studies to withdraw samples for additional analysis. Then remove the needle, either after reinserting the stylet or with the syringe attached.

Detection of CD133 expression in acute leukemia by flow cytometery: Cells were stained by direct immunofluorescence using the phycoerythrin (PE)-conjugated Moab AC1336 (IgG1; Miltenyi Biotech, Auburn, CA, USA) and the FITC-conjugated anti-CD90 Moab 5E10 (IgG1; PharMingen, San Diego, CA, USA) as recommended by the manufacturers. Non-specific binding of Fcγ-receptors was blocked by pre-incubation of the cells with a polyclon-al rabbit serum (Gibco BRL, Paisley, UK). At least 10,000cells per sample were acquired and analyzed by flowcytometry as described above. Freshly obtained cell sam-ples were analyzed by three-color immunophenotyping, using CD90-FITC, CD133-PE and CD34-PECy5 (cloneQBend10; Beckman Coulter, Marseille, France). Non-viable cells were excluded by scatter gating. Previously cryopreserved cell samples were analyzed by two-colorimmunophenotyping using the combinations CD90-FITC/CD34-PE, CD34-FITC/CD133-PE, and CD90/CD133-PE. In each of these samples, non-viable cells were excluded from analysis by propidium iodide co-staining (0.3 μg/mL; Sigma, Deisenhofen, Germany). No differ-ences in CD133 and CD90 expression patterns between freshly obtained and previously cryopreserved cell samples were observed. Cell samples were considered posi-tive for CD90 or CD133 if at least 20% of the leukemic cells/sample specifically stained with Moabs 5E10 orAC133 revealed a higher fluorescence intensity than cells stained with the isotype-matched control antibody(=20% cut-off level).

Ethical considerations: Informed written consent had been obtained from all patients participating in the study. The Study was approved by Ethical Committee Board of Ain Shams University and in accordance with Declaration of Helsinki.

Time schedule:

Data management and Statistical Analysis: Data collected throughout history, basic clinical examination, laboratory investigations and outcome measures coded, entered and analyzed using Microsoft Excel software. Data were then imported into Statistical Package for the Social Sciences (SPSS version 20.0) (Statistical Package for the Social Sciences) software for analysis. According to the type of data qualitative represent as number and percentage, quantitative continues group represent by mean ± SD, the following tests were used to test differences for significance;., correlation by Pearson's correlation or Spearman's. P value was set at

Data were collected and submitted to statistical analysis. The following statistical tests and parameters were used.

ROC curve: Receiver operating characteristic (ROC), or simply ROC curve, is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied. It is created by plotting the fraction of true positives out of the positives (TPR = true positive rate) vs. the fraction of false positives out of the negatives (FPR = false positive rate), at various threshold settings. TPR is also known as sensitivity (also called recall in some fields), and FPR is one minus the specificity or true negative rate.

ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. ROC analysis since then has been used in medicine, radiology, biometrics, and other areas for many decades and is increasingly used in machine learning and data mining research

RESULTS

Table 1: Comparison between studied groups as regard Demographic data

Table 2: Distribution of studied cases as regard Clinical data

Table 3: Comparison between studied groups as regard CBC

Table 4: Comparison between studied groups as regard Liver and kidney function tests

Table 5: Comparison between studied groups as regard viral screening tests

Table 6: Distribution of studied cases as regard CD133

Table 7:      Comparison between negative and positive CD133 according to CBC

IQR: Inter quartile range                          SD: Standard deviation           

t: Student t-test                             U: Mann Whitney test

p: p value for comparing between negative and positive CD133

This table shows that there is no significant difference between negative and positive CD133 according to clinical data

Table 8: Distribution of studied cases as regard outcome

Table 9: Relation between CD133 and outcome

DISCUSSION

Acute lymphoblastic leukemia (ALL) has become curable disease in more than 80% of patients; however, the treatment results in significant morbidity and mortality. The use of risk‑adapted treatment protocols has improved cure rates while limiting the toxicity of therapy (Glumac et al., 2018).

The discovery of cancer stem cells (CSCs) has played a pivotal role in changing the view of carcinogenesis and chemotherapy. Several markers have been identified for characterization of CSCs. Prominin‑1 is a cell‑surface trans‑membrane glycoprotein expressed on the surface of stem cells, including normal and CSCs. Prominin‑1 represents a marker of CSCs that has been shown to be more specific in hematopoietic malignancies than CD34 and may provide alternative to the usual CD34 monoclonal antibodies. In contrast to CD34 antigen, Prominin‑1 antigen is lost very early during the differentiation process (Hagag et al., 2016).

Developments in flow cytometric techniques and the availability of lineage-associated monoclonal antibodies have permitted characterization of normal and leukemic cells and affirmed the immunophenotypic heterogeneity in ALL. Evidence suggests that ALL has a primitive cell origin and shares many immunophenotypic characteristics with normal progenitor cells. These leukemic stem cells may be resistant to current therapeutic strategies, and subsequent relapses may arise from this population. These cancer stem cells have been shown to express CD133 (AC133), a primitive cell antigen, that has been shown to be more specific marker of hematopoietic stem cells than CD34 (Valent et al., 2020).

There have been conflicting reports on the expression of CD133 in ALL, whereas some found high levels of CD133 expression on particular cases. Others detected only few levels or none at all. The current classification of acute myeloid leukemia (AML) is based predominantly on the cytogenetic abnormalities and morphology of malignant blasts but it is not always helpful for optimization of the treatment strategy (Farid et al., 2019).

Human CD133 (AC133) is a novel five-transmembrane molecule, which is expressed on primitive normal hematopoietic progenitor. AC133 reacts with a population of non-committed or granulomonocytic GM-committed CD34? cells in normal hematopoiesis. CD133 reactivity was observed in cases of AML, especially myelomonocytic different AML FAB M4/M5 cases. In the hematopoietic system, CD133 is expressed on a subset 30–70 % of the CD34? cells in the human bone marrow, fetal liver, umbilical cord blood and growth factor primed peripheral blood. The expression of CD133 on primitive AML cells is unknown, while it has been reported that CD133 is expressed on the majority of bulk CD34? AML cells, whereas CD133 expression on CD34- AML cells is low to absent in most but not all cases (Handschuh et al., 2019).

Prominin‑1 positive cells are expressed in both acute and chronic myeloid and lymphoid leukemia of adult and pediatric patients. High expression of Prominin‑1 may be an adverse prognostic factor in patients with acute leukemia, which is associated with lower complete remission (CR) and overall survival (OS) rates. Thus it seemed reasonable to undertake this study to assess the prognostic value of Prominin‑1 (CD133) expression in Egyptian children with ALL (Riegg et al., 2021).

The aim of the present study is to know about PROM-1 (CD 133) overexpression in adult acute lymphoblastic leukemia Egyptian patients and relation to outcome.

In the current study we found that there was no statistically significant difference between the studied groups as regards Age (years) and sex.

In agreement with our results, Hagag et al. (2016) showed that there was no statistically significant difference between the studied groups regarding age and sex.

In contrast to our results, Tolba et al. (2013) reported that there was no significant difference between age of patients and positivity for CD133 expression. There were (18) males and (12) females with a male to female ratio of 1.5:1 and their ages ranged from 9 months to 48 years with the mean 15.8 ± 12.4, while in AML group, they were ranged from 17 to 66 years, with a mean age of 34.3 ± 15.2 years. They were 15 males and 15 females with a male to female ratio of 1:1.

Matching our results, Horn et al., Lee et al. (2001) and Guenova et al. (2008) found that no significant association was detected between the gender and positive CD133 expression in their study.

In our study we found that there were 26 (81.2%) with fever, 10 (31.2%) with Bleeding tendency, 26 (81.2%) with Lymph node, 17 (53.1%) with Hepatosplenomegaly, 9 (28.1%) with Mediastinal mass and 17 (53.1%) with Bone pain.

Tolba et al. (2013) illustrated that there were 24 (80.0 %) with pallor, 22 (73.3 %) with fever, 8 (26.7 %) with bleeding tendency, 22 (73.3 %) with lymph node, 17 (56.7 %) with Mediastinal mass and 20 (66.7 %) with bone pain.

Hagag et al. (2016) documented that there were no statistically significant differences between Prominin‑1 positive and negative patients regarding age, sex and clinical presentation at the time of diagnosis, including pallor, purpura, hepatomegaly, splenomegaly, and lymphadenopathy and CNS infiltration.

These results were in agreement with Zhou et al. (2005) and Elgendi et al. (2010) who found no significant differences between Prominin‑1 positive and negative patients regarding age and sex and clinical presentation at the time of diagnosis.

In the present study we found that there was no statistically significant difference between the studied groups as regard Hb, WBCs, Plts, PT, blast and ESR.

matching to our results, Hagag et al. (2016) reported thatthere were no significant differences between Prominin‑1 positive and negative expression regarding initial WBCs and platelet counts, percentage of blast cells in peripheral blood and BM, but there were significantly higher Hb and LDH levels in Prominin‑1 positive patients.

These results agree with Wang et al. (2007) who found no significant association between Prominin‑1 expression and any of the laboratory variables. Guenova et al. (2008) and Elgendi et al. (2010) found no correlation between Prominin‑1 and laboratory variables, including WBCs count and Hb levels; however, they found significant association between Prominin‑1 positive expression and percentage of peripheral blood blasts among ALL patients.

Heincelman et al. (2016) showed that laboratory data revealed a white blood cell count 14 400/ cm3 (34% neutrophils, 18% lymphocytes, 33% reactive lymphocytes, 2% eosinophils), hemoglobin 18.3 g/dL, hematocrit 52%, and platelet count 165 000/cm3.

Our findings regarding Liver and kidney function testsrevealed thatthere was no statistically significant difference between the studied groups as regard Liver and kidney function tests. There was no viral infection detected evaluated the mean reductions in intraindivid- ual AST and ALT over time by using difference scores from baseline to Day 28 and from baseline to Day 56. No significant differences between MT and placebo in AST or ALT from baseline to Day 28 (P ¼.55; P ¼.50, respectively) were observed. At Day 56, the MT group had a significantly lower AST (P ¼.05) and a trend to- ward a significantly lower ALT (P ¼.07) from baseline than the placebo group.

Similar results were reported by Ladas et al. (2010) who evaluated that the mean reductions in intraindividual AST and ALT over time by using difference scores from baseline to Day 28 and from baseline to Day 56. No significant differences between MT and placebo in AST or ALT from baseline to Day 28 (P = 0.55; P =0.50, respectively) were observed. At Day 56, the MT group had a significantly lower AST (P =.05) and a trend to-ward a significantly lower ALT (P =.07) from baseline than the placebo group.

Heincelman et al. (2016) showed that metabolic panel revealed a creatinine 2.0 mg/dL, total bilirubin 1.6 mg/dL, aspartate aminotransferase 263 IU/L, alanine aminotransferase 893 IU/L, and alkaline phosphatase 192 IU/L. The international normalized ratio was 1.03. Creatinine kinase was 11 IU/L. Hepatocellular injury is commonly observed in ALL patients during their treatment course secondary to liver injury from medications, post-chemotherapy viral infections (especially Hepatitis B), sepsis, and perhaps even ischemia.4 However, while hepatitis is a well-known complication during treatment of ALL.

Segal et al. (2010) reported that in examining hepatitis at the time of ALL diagnosis, roughly one third of patients had increased aminotransferases with normal bilirubin and alkaline phosphatase (ie, laboratory values consistent with hepatitis) without evidence of viral hepatitis (negative HAV, HBV, HCV, HSV, EBV, and CMV). ALL-induced hepatitis was found to be more common in patients with T-cell ALL and in patients with higher white blood cell counts, uric acid levels, and lactate dehydrogenase levels, suggesting tumor infiltration as the underlying etiology. Further support stems from resolution of hepatitis in all subjects with treatment

In our study we found that there were 12 (37.5%) with positive CD133 with mean 15.28 (±24.62 SD) and range (0.2-89.2).

Tolba et al. (2013) documented that CD133 was positive in 10/30 (33.3 %) cases only of ALL patients, and the frequency of CD34 expression was proved to be found on 100 % of CD133-positive cases. These groups of patients show high degree of immaturity as TDT was also 100 % expressed on cases with CD133 positive. To elucidate the value of CD133 expression as a prognostic factor in acute leukemia, we investigated the significance of its expression in relation to various clinical, laboratory and standard prognostic factors, as well as to treatment response and clinical outcome of patients. No significant difference was noted between age of patients and positivity for CD133 expression. This is in concordance with Horn et al., Lee et al. and Guenova and Balatzenko.

Different laboratory variables showed no significant associations with CD133-positive expression except TLC. These results are in similarity to those reported by Zhou et al. (2005), who found no significant association between CD133 expression and any laboratory variables.

Tolba et al. (2013) revealed that to further elucidate the prognostic significance of CD133; prognosis was studied in relation to CD133 expression. Its expression was significantly related to the response of chemotherapy, where a statistically significant increase in the number of non-responders who had positive CD133 expression 4/10 (40 %) was detected when compared to non-responders who had negative CD133 expression 2/17 (10 %); these results are consistent with reports by Zhou et al. (2005) who stated that CD133-positive expression is an independent poor prognostic factor in childhood acute leukemia and could characterize a group of patients with resistance to standard chemotherapy, as well as high incidence of relapse and death Elgendi et al. (2010).

In the present study there was statistically significant Relation between CD133 and outcome.

In our study, there were statistically significant differences in disease outcome between Prominin‑1 positive and negative expression with higher rate of relapse and death and lower rate of complete remission, disease free survival and overall survival in Prominin-1 positive expression compared with Prominin-1 negative expression. This is in agreement with Zhou et al. (2005) who found lower CR and survival rates in Prominin‑1 positive cases especially if co‑expressed with CD34, Cox et al. (2009) who found the same results and explained the poor clinical outcomes associated with positive Prominin‑1 expression by increased resistance of Prominin‑1 positive cells to chemotherapeutic agents especially dexamethasone and vincristine, which in turn is attributed to higher expression of multidrug resistance gene, breast cancer resistance protein1 (BCRP1) as well as genes that inhibit apoptosis in the Prominin‑1 expressing CSCs and Mak et al. (2012) who studied the functional role of Prominin‑1 in the MLL‑AF4– dependent ALL cells and found that Prominin‑1 was required for leukemia cell survival and therefore may represent a bad prognostic factor in patients with ALL.

Tolba et al. (2013) showed that the value of CD133 in determining the clinical outcome, the positivity of CD133 expression at diagnosis was compared between the patients being stratified according to their clinical outcome. Eighty-five percent (17 patients out of 20) of ALL patients who achieved complete remission were negative for CD133 expression on their blasts, while, on the contrary, 10 % (1/10) of ALL cases that died by the end of the follow-up period had positive CD133 expression. Moreover, 40 % (4 out of 10) of ALL cases that developed resistance to chemotherapy were positive for CD133 expression.

These results are consistent with reports by Lee et al., as well as Zhou et al. (2005) and Elgendi et al. (2010), who found a trend toward higher CR rates in CD133-negative ALL cases when compared to CD133-positive ones. Furthermore, Horn et al., as well as Elgendi et al., reported a tendency for poorer outcomes in CD133-positive ALL compared to CD133-negative ones.

From the current study, we concluded that Prominin‑1 positive expression is a helpful prognostic marker in patients with ALL. Prominin‑1 should be routinely assessed at diagnosis in ALL patients for better prognostic assessment and should be taken in consideration in designing future therapeutic strategies based on patient‑specific risk factors.

CONCLUSION

Prominin 1 positive expression is a helpful prognostic marker in patients with ALL. Prominin 1 should be routinely assessed at diagnosis in ALL patients for better prognostic assessment and should be taken in consideration in designing future therapeutic strategies based on patient specific risk factors