Taking High-Tech to the Field: Leukemia Diagnosis in Pediatric Mexican Patients from Vulnerable and Remote Regions

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Taking high-tech diagnosis to the field: leukemia diagnosis in pediatric Mexican patients
from vulnerable and remote regions

My comments and suggestions are the following:

1. The manuscript presents a large-scale, nationwide implementation of a decentralized diagnostic
model for pediatric leukemia. While the descriptive results are compelling, could the authors
clarify how consistency and quality control were maintained across different OncoCREAN
centers, particularly during sample collection, handling, and shipment from geographically remote
regions?

2. The study reports substantial reductions in diagnostic turnaround time and improvements in
early survival outcomes. Could the authors provide a clearer comparison between outcomes before and after OncoCREAN implementation, including baseline diagnostic timelines or historical
controls, to better quantify the magnitude of improvement attributable to this model?

3. The epidemiological findings reveal regional differences in leukemia subtypes and age of onset.
Could the authors expand the discussion on potential contributing factors, such as socioeconomic
conditions, environmental exposures, or healthcare access disparities, and clarify how these
insights could inform region-specific public health interventions?

4. The manuscript highlights the translational research potential of the OncoCREAN and OCL
framework, including the creation of biological repositories and data atlases. Could the authors
elaborate on how these resources will be integrated into future hypothesis-driven studies or clinical decision-making pipelines, and how data sharing and governance will be managed long term?

5. The manuscript would benefit from a broader discussion situating the OncoCREAN strategy
within the context of recent advances in technology-assisted diagnosis in other medical imaging
domains. Incorporating related and recent articles, such as work on “Automated multi-class
classification of skin lesions through deep convolutional neural network with dermoscopic
images” and “Automated identification of human gastrointestinal tract abnormalities based on
deep convolutional neural network with endoscopic images”, alongside other relevant literature,
could strengthen the discussion by highlighting shared challenges in deploying advanced
diagnostics at scale, improving accessibility in underserved populations, and translating high-
technology tools into real-world clinical settings.

Author Response

We thank Reviewer 1 for their suggestions and comments, which were very valuable and helped to considerably improve the manuscript.

1. The manuscript presents a large-scale, nationwide implementation of a decentralized diagnostic model for pediatric leukemia. While the descriptive results are compelling, could the authors clarify how consistency and quality control were maintained across different OncoCREAN centers, particularly during sample collection, handling, and shipment from geographically remote regions?

We appreciate the reviewer’s invaluable comment.

Indeed, as a reference center, we ensure that sample collection, shipment, and processing are carried out under strict conditions to guarantee the reliability of the results. The pre-analytical, analytical, and post-analytical phases are fully traceable to identify any deviation in the process and ensure the validity of the outcome.

The OncoCREAN reference center, through the Oncoimmunology and Cytomics Lab (OCL), has developed and disseminated to its 32 affiliated Centers the Guide for Sample Shipment: Immunophenotype and Minimal/Measurable Residual Disease Study. Likewise, the requirements for sample collection in patients with suspected childhood leukemia are aligned with the institutional algorithms and clinical practice guidelines established by the Mexican Social Security Institute and the Ministry of Health.

During the analytical phase, and as part of the EuroFlow protocols, rigorous quality controls have been implemented through the full standardization of flow cytometry methodology for the diagnosis and monitoring of hematologic diseases. These include:

1. Standardized Operating Procedures (SOPs): The consortium developed uniform and detailed technical protocols for sample preparation, instrument setup and calibration (flow cytometers), and data analysis [1].
2. Harmonized Antibody Panels: EuroFlow has defined scientifically validated and standardized antibody panels that are essential for ensuring reproducibility of results across different laboratories [2].
3. Multicenter Evaluation: The reproducibility of results obtained with these panels and protocols is ensured through multicenter evaluations, in which multiple laboratories participate to validate methodological consistency.
4. Computational Tools and Automation: The use of advanced computational tools and automated data analysis has been fundamental for efficiently managing large data volumes and ensuring reproducible results.

We added this information to the current version of the manuscript. Additionally, we added the guide as the supplementary file.

2. The study reports substantial reductions in diagnostic turnaround time and improvements in early survival outcomes. Could the authors provide a clearer comparison between outcomes before and after OncoCREAN implementation, including baseline diagnostic timelines or historical controls, to better quantify the magnitude of improvement attributable to this model?

We thank the reviewer for the insightful comment. This information has been added to Discussion section of the manuscript.

Since March 2022, when OCL began operations with the reception of its first diagnostic sample, the first-year overall survival rates among patients diagnosed that year varied notably based on the type of monitoring received. Early mortality was lower in patients who underwent MRD testing at the OCL compared to those who did not (10.3% vs. 24.6%, p < 0.01) [3]. These findings are particularly relevant in LMICs, where early treatment-related deaths continue to pose a significant barrier to survival, and where the implementation of diagnostic and prognostic tools, such as immunophenotyping and flow cytometry-based MRD monitoring, could have a substantial clinical impact and save lives

Notably, nearly all patients who were treated based on clinical immunophenotyping at diagnosis and subsequently re-stratified according to MRD findings survived during the first year after diagnosis confirmation (96.2%) in comparison with those not analyzed in the OCL (73.3%) (Log-Rank Test; p-value <0.001) [3]. To further explore factors independently associated with early mortality, a multivariable Cox regression analysis was performed including MRD monitoring at OCL, sex, NCI risk classification, treatment abandonment, and early relapse. The results showed that MRD monitoring at the OCL remained a significant protective factor against early death, even after adjusting for other clinical variables (adjusted hazard ratio [aHR] 0.41; 95% CI: 0.22–0.77; p < 0.01) [3].

In all cases, MRD assessments at the OCL were communicated to attending physicians within 72 hours, enabling timely and actionable clinical interventions: patients with detectable MRD received post-induction therapy intensification and closer monitoring, whereas patients with non-detectable MRD  continued standard treatment protocols [3].

Studies performed in B-ALL at diagnosis have shown that CD34 antigen expression was independent of conventional risk factors, such as cytogenetic group, white blood cell count, and age. In addition, immunophenotyping performed in OCL define three additional B-ALL subtypes based on the expression of the stemness marker CD34. The ProB (CD34+) subtype was significantly more likely to present with detectable MRD after induction, suggesting a more resistant leukemic profile. In contrast, the PreB (CD34−) subtype was associated with lower MRD detection rates and potentially more favorable responses to initial chemotherapy [3].

Consistently with that, a study by Modvig et al identified the CD34⁺CD38^dim/+TdT ^dim/+ immunophenotype as a predictor of poor induction response and MRD positivity postinduction. In this study, CD34 expression correlated with increased relapse risk and progressively increased from diagnosis to relapse. CD34+ leukemias exhibited overexpression of genes associated with stemness, migration, adhesion, and survival [4]. This study was the first to establish that a CD34-negative immunophenotype is a favorable prognostic factor in B-ALL.

Additionally, Kulis et al. applied machine learning methods to identify antigen expression patterns that reveal potential genetic abnormalities in B-ALL cases. The combination of low antigen expression levels, particularly CD10, CD34, and TdT, has been associated with the occurrence of KMT2A rearrangements, suggesting differential antigen expression as additional markers for disease characterization [5]. The deep analysis of expression levels of various markers provided by the standardized immunophenotype, like CD10, CD66c, and CD9, and their relationship with CD34 may serve as a valuable tool for risk stratification at the time of diagnosis. However, its potential association with other molecular modifications has not yet been explored in our population.

Thanks to the OncoCREAN strategy, 90% of diagnoses were made in less than seven days, and more than 90% of treatments started within three days of diagnosis, which has been key to improving clinical outcomes. One of the most significant achievements has been reducing the abandonment of treatment rate to less than 2%, in contrast to other institutions where this figure reaches up to 40%. Furthermore, the survival rate has risen to over 84%, thanks to timely care, effective management of complications, and the use of cutting-edge therapies [6].

In various hematological malignancies, immunophenotyping by flow cytometry is essential for informed medical decision-making. The effectiveness of interpreting the results is inversely proportional to the variability in their presentation. To achieve uniformity in the reporting of immunophenotypes nationwide, one strategy of the OCL was to harmonize results reports by using a single reporting form [7]. This data harmonization has allowed for consistent communication among the 28 states that send samples to the laboratory, integrating the immunophenotypic information of tumor populations and thus supporting informed clinical decisions.

3. The epidemiological findings reveal regional differences in leukemia subtypes and age of onset. Could the authors expand the discussion on potential contributing factors, such as socioeconomic conditions, environmental exposures, or healthcare access disparities, and clarify how these insights could inform region-specific public health interventions?

We thank the reviewer for the insightful comment. This information has been added to Discussion section of the manuscript.

The incidence and prevalence of acute leukemias in Latin America, in which all countries are either low-income countries (LIC) or MIC, appear to be similar. This similarity seems to be secondary to environmental factors, such as early infections from childhood, exposure to organophosphate pesticides and many other potential risk influences such as genetic polymorphism [8]. A phenomenon associated with access to health services is that while cancer incidence rates are higher in urban areas, mortality rates are higher in rural areas, reported factors that contribute is that patients in rural areas are frequently diagnosed at later stages, are less likely to received standard treatments, optimal medical follow-up, or support services, and consequently experience worse health outcomes than urban patients [9]. Additionally, the highest rates of treatment abandonment are recorded in hospitals with the lowest Human Development Index, an index that includes measurements of education, health, and income per person [8]. The reasons for treatment abandonment include poverty, lack of interest in their own disease, cultural myths, feelings of guilt, and social discrimination among their peers [10]. A prospective cohort study of 574 patients under 18 years diagnosed with AL, conducted in Oaxaca, Puebla and Tlaxcala from 2021 to 2023, showed that living ≥141 km from the hospital (adjusted OR = 1.68; 95% CI: 1.02–2.74; p = 0.03) were significantly associated with abandonment of treatment [11]. Moreover, human resources and infrastructure should be taken into consideration in the care of children with cancer. There is a reduced number of pediatric hematologist/oncologist across all medical systems nationwide, a comparison in 2011 with the United States showed that in that country one specialist registers 7.84 new patients per year while in ours 46.4 new patients per year[8]. A retrospective cohort study from Mexico’s Seguro Popular program, covering cancer treatment from 2005 to 2015 estimated a 5-year overall survival of 61.8%, with ranges from 74.7% to 43.7% among the states. They found a higher risk of mortality for children who received treatment in a non-pediatric specialty hospital (Hazards Ratio, HR = 1.18; 95%CI 1.09, 1.26), facilities without a pediatric oncology/hematology specialist (HR = 2.17; 95%CI 1.62, 2.90), and hospitals with low patient volume (HR = 1.22; 95%CI 1.13, 1.32). While 5-year risk-standardized survival was above 70% for Sinaloa, Michoacán, and Zacatecas, there were seven states where survival was 50% or below (Hidalgo, Puebla, Chiapas, Tabasco, Oaxaca, Veracruz and Campeche) [12]. A high proportion of the population in the states of Puebla and Oaxaca live in poverty (62.4% and 61.7% respectively) frequencies that are higher than the national average (36.3%). The Mixteca region, an area connecting the capital cities of these states, showed an AL incidence rate of 96.7 and 88.5 cases per million, for groups 0-14 and 0.19, respectively[9]. Another study showed consistent findings regarding socioeconomic conditions in Mexico City and AL, they observed the highest rates of AL in municipalities located mainly at Mexico City east, which are characterized by a low socioeconomic status (SES). Contrary, low incidence rates of AL were observed in municipalities with a better SES. SES can not be separated from the ethnic composition, the lower the SES of a Mexican individual, the higher the probability to have an Amerindian ancestry [13]. Additionally, in Mexico there is a coexistence of malnutrition and obesity, termed the double burden of malnutrition, has been suggested to predict early mortality in childhood ALL, presumably by a mechanism of leukemic cell protection from chemotherapy by adipocytes and inflammaging-like signature. According to UNICEF, our nation is the first consumer of sugar drinks and processed food, especially in extreme poverty families [14].

Research on environmental factors associated with pediatric leukemia has historically focused on analyzing a limited number of exposures considered in isolation, which has restricted the understanding of a multifactorial etiological process. In some previous studies, childhood ALL was more frequent in urban areas than in certain rural areas of the USA [15]. Similarly, studies by González-García et al. describe a higher incidence of childhood cancer in boys, comparable to that observed in developing countries. However, the authors emphasize the need to further investigate the causes of the higher proportion of girls affected by leukemia in the 0-4 age group, as well as the survival differences observed in boys aged 0-14 years between rural and urban areas of this population [16].

These findings can be interpreted in light of the model proposed by Greaves, who suggests that childhood ALL could be considered a paradoxical consequence of progress in modern societies, where lifestyle changes have limited early exposure to microorganisms. This restriction creates complications in the evolutionary adaptation of the immune system and contemporary lifestyles, contributing to the risk of leukemogenesis [17]. This conceptual framework reinforces the need to analyze environmental exposures not as independent factors, but as interrelated components within complex biological and social systems.

Among the environmental exposure factors studied, maternal prenatal exposure has gained particular relevance due to the description of the appearance of pre-leukemic clones in utero. Among these, indoor home renovations or contact with pesticides during pregnancy have been associated with an increased risk of childhood ALL. Subgroup analyses stratified by sex, age at diagnosis, and other factors support these associations [18]. Along these same lines, statistically significant differences have been identified between children with ALL and controls in the levels of organophosphate metabolites derived from pesticides, such as diethylthiophosphate (P < 0.03) and diethyldithiophosphate (P < 0.05) [19]. However, no interactions were detected between exposure to indoor ventilation and pesticides in additive models, nor were significant links found with maternal passive exposure to tobacco smoke, the use of antipyretic analgesics, or viral infections during pregnancy [18]. These results suggest that the effect of certain exposures may depend on the biological and environmental context in which they occur, rather than on their individual action.

Consistently, in Brazil, early-onset leukemia has been associated with maternal occupational exposure to chemicals, particularly in the agricultural, chemical, and petrochemical sectors [20], reinforcing the relevance of occupational and social determinants within the continuum of environmental exposures.

While evidence on the relationship between environmental benzene exposure and pediatric leukemia remains limited, recent data suggest a possible role for this organic compound in the risk of childhood hematological malignancies, especially in areas with persistent sources of natural and industrial environmental pollution [21]. These findings underscore the need for better-designed epidemiological studies in pediatric populations to understand the underlying mechanisms and estimate specific risks. They also reinforce the importance of public policies aimed at reducing environmental benzene exposure as part of comprehensive prevention strategies.

To date, there are no sufficiently defined environmental targets that would allow for the design of specific regional policies with a clearly demonstrated impact on the incidence of childhood acute leukemia, since isolated associations do not necessarily imply causality. Given that ionizing radiation remains the only environmental agent with solid causal evidence, public policy should focus on strategies aimed at minimizing unnecessary exposures, particularly occupational exposure, as well as strengthening early detection and reducing regional inequalities in access to diagnosis and treatment. In line with Greaves' proposal, progress in understanding the causes of ALL should be supported by a deep understanding of cancer biology and the integration of multidisciplinary research and international consortia, with the goal of informing more robust public health policies and the development of more effective preventive interventions.

In this context, the accumulated knowledge on contributing environmental factors in the etiology of pediatric leukemia, integrated with the IMSS OncoCREAN epidemiological registry, represents a strategic opportunity to contribute to the design and strengthening of public policies in Mexico. The availability of high-quality clinical, sociodemographic, and immunophenotypic data will allow not only the description of the disease burden but also the analysis of spatial and temporal patterns of incidence, as well as its possible association with relevant environmental exposures in different geographic areas of the country.

The OncoCREAN registry offers a unique platform to move from isolated associations to integrative models that consider the interaction between environmental, biological, social, and developmental factors. In a country with high geographic, socioeconomic, and environmental heterogeneity like Mexico, this approach will be innovative for identifying vulnerable pediatric populations and risk scenarios, generating essential local evidence for public health decision-making.

Finally, the use of the OncoCREAN registry as a tool for translational research and epidemiological surveillance can help bridge the gap between the generation of scientific knowledge and its application in public policy. In this way, the Mexican Social Security Institute (IMSS) can play a key role not only in the care and treatment of pediatric leukemia, but also in developing national strategies for prevention, ensuring equitable access to care, and reducing the burden of childhood cancer in Mexico.

4. The manuscript highlights the translational research potential of the OncoCREAN and OCL framework, including the creation of biological repositories and data atlases. Could the authors elaborate on how these resources will be integrated into future hypothesis-driven studies or clinical decision-making pipelines, and how data sharing and governance will be managed long term?

Thank you for the constructive suggestion. This information has been added to the second section of the Results and to the Discussion part.

Within the PRONAII, six different research groups were facilitated with patient samples and clinical data for their research from our PRONAII cell repository. To request samples and/or data, the responsible researcher must be contacted, and a confidential commitment letter has to be signed [22]. The purposes of using the samples in the different projects include the evaluation of new diagnostic and prognostic tools, searching for possible therapeutic targets, conducting genomic studies, analyzing snoRNA expression, evaluating new pharmacological targets, and studying the tumor microenvironment, from the creation of bad prognosis predictive profiles to how environmental contaminants affect the hematopoietic microenvironment [3,9,23–25].

Cell repositories play a crucial role in advancing biomedical research, from drug development to regenerative medicine [26]. The accuracy, reproducibility, and ethical rigor of biomedical research depend on how samples are collected, stored and documented [27].

Ethics approval documentation is vital, serving as evidence that all material was obtained in accordance with ethical guidelines and regulations, which includes proof of informed consent from donors where applicable and adherence to institutional review board (IRB) protocols. This provides transparency and confidence within the process [26]. Since we received samples from all over the country, ethical approval from patients and legal tutors to keep their samples in storage is a major challenge. Social service medical interns are required to collect informed consents within the OncoCREAN units. So that a sample can be stored in our repository must fulfill these requirements.

Implementing structured sample storage policies and infrastructure is crucial for maintaining scientific quality, ensuring regulatory compliance, and promoting responsible research conduct [27]. Therefore, we are committed to continuing with good practices and a continuous transition to a formal biobank.

The implementation of the OCL has significantly contributed to reducing early mortality. Our research group demonstrated that, following the establishment of the OCL, the survival rate increased to 83.3% among patients whose diagnostic and follow-up tests were both conducted at the OCL, compared to 69.4% in those whose tests were not[3].  This substantial impact prompted the designation of the OCL as the reference laboratory for all OncoCREAN centers, owing to its standardized protocols for diagnostic and follow-up testing. Furthermore, the OCL periodically provides epidemiological data to institutional authorities to facilitate evidence-based and timely decision-making. In addition, for cases of high clinical complexity, multidisciplinary meetings are convened with treating physicians and specialized experts to ensure optimal patient management.

5. The manuscript would benefit from a broader discussion situating the OncoCREAN strategy within the context of recent advances in technology-assisted diagnosis in other medical imaging domains. Incorporating related and recent articles, such as work on “Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images” and “Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images”, alongside other relevant literature, could strengthen the discussion by highlighting shared challenges in deploying advanced diagnostics at scale, improving accessibility in underserved populations, and translating high- technology tools into real-world clinical settings.

We have attended the comment and we truly belief that the incorporation of the recent advances in technology improved our manuscript. Thank you for the suggestion.

Recent advances in image processing and artificial intelligence (AI) algorithms for disease diagnosis and prognosis have improved the chances to survive of cancer in its many forms. In diagnostic imaging, AI algorithms can detect tumors with high precision, assisting radiologists and pathologists in interpreting radiological images and tissue samples more accurately and swiftly [28]. Some examples include melanoma , where skin lesion classification has a major role in the early and accurate diagnosis of skin cancer and new specialized models based on dermoscopic images have been developed [29]; another important model is the Multimodal transformer with Unified maSKed modeling (MUSK), that showed strong performance in predicting clinical outcomes, including melanoma relapse, pan-cancer prognosis and immunotherapy response predictions [30].

In leukemia other efforts have been made to provide predict patient’s prognosis. Unlike solid tumors, the samples frequently used in leukemia are liquid, and while an image-based test is not the most common for this malignancy, these types of models based on the phenotype of the patient´s malignant cell populations can also be made, such is the case of the DDPR model [31], that allows to predict relapse at diagnosis for B cell precursor acute lymphoblastic leukemia through mass cytometry. In a collaborative effort with Dr. Davis group from Stanford University, we are currently working in a predictive model adapted to our country, including other parameters like tumor microenvironment. Further studies with this approach must be done. Cancer patients in different regions exhibit significant differences in genetic backgrounds, environmental factors, and lifestyles [28]. Global data sharing contributes to assess new technologies efficiency, one of our goals is to integrate our data in the global knowledge of the disease, particularly to provide insights into other countries with similar economies and social problems like Mexico.

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25. Juárez-Avendaño, G.; Méndez-Ramírez, N.; Luna-Silva, N.C.; Cruz-Hernández, V.A.; Gómez-Almaguer, D.; Pelayo, R.; Balandrán, J.C. Molecular and Cellular Markers for Measurable Residual Disease in Acute Lymphoblastic Leukemia. Bol. Med. Hosp. Infant. Mex. 2021, 78, 159–170, doi:10.24875/BMHIM.20000155.
26. Weiskirchen, S.; Monteiro, A.M.; Borojevic, R.; Weiskirchen, R. Unlocking Potential: A Comprehensive Overview of Cell Culture Banks and Their Impact on Biomedical Research. Cells 2024, 13, doi:10.3390/CELLS13221861.
27. Carranza, C.; Juárez, E.; Carreto-Binaghi, L.E.; Chávez-Domínguez, R.L.; García-Ramos, M.A.; Viettri, M.; Corona-Galvan, I.A.; Zamudio-Meza, H.; Reyna-Rosas, E.; Nieto-Ponce, M.; et al. Biological Sample Storage in Biomedical Research. Clinica Chimica Acta 2026, 578, 120538, doi:10.1016/J.CCA.2025.120538.
28. Wang, M.; Chang, W.; Zhang, Y. Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges. MedComm (Beijing). 2025, 6, e70460, doi:10.1002/MCO2.70460;SUBPAGE:STRING:FULL.
29. Iqbal, I.; Younus, M.; Walayat, K.; Kakar, M.U.; Ma, J. Automated Multi-Class Classification of Skin Lesions through Deep Convolutional Neural Network with Dermoscopic Images. Computerized Medical Imaging and Graphics 2021, 88, doi:10.1016/j.compmedimag.2020.101843.
30. Skourti, E. A Vision–Language Foundation Model for Clinical Oncology: Digital Pathology. Nat. Cancer 2025, 6, 226, doi:10.1038/S43018-025-00923-4;KWRD.
31. Good, Z.; Sarno, J.; Jager, A.; Samusik, N.; Aghaeepour, N.; Simonds, E.F.; White, L.; Lacayo, N.J.; Fantl, W.J.; Fazio, G.; et al. Single-Cell Developmental Classification of B Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse. Nat. Med. 2018, 24, 474–483, doi:10.1038/NM.4505;TECHMETA.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Affiliations: Please write the english version of affiliations

Abstract: What is OCL? write definition

Introduction: please write definition of LMIC

Please give some information about the population of Mexico, population of patients with cancer, the survival rates, complication rates and other some statistical informations so that your study can gain importance

REsults: ...Among received samples only 45 (1.3%) were non-evaluable for result interpretation, in which case the pediatric hematologist or oncologist was notified, and a new BM aspiration was requested for processing. .... add some comment and give more details for this high rate

Discussion: it is weak compared to other sections; please expand it with more studies and discuss in detail

 

Comments on the Quality of English Language

needs improved

Author Response

We thank Reviewer 2 for their suggestions and comments, which were very valuable and helped to considerably improve the manuscript.

1. Abstract: What is OCL? Write definition

We apologize for the error and thank the reviewer for the opportunity to correct it. OCL means Oncoimmunology and Cytomics Laboratory.

2. Introduction: please write definition of LMIC

We thank the reviewer for bringing attention to this acronym. We made a correction in the Abstract, it was “low- to middle-income countries” to “low & middle income countries” and the definition “economies with a gross national income (GNI) per capita lower than $13,935” was added in the Introduction.

3. Please give some information about the population of Mexico, population of patients with cancer, the survival rates, complication rates and other some statistical information so that your study can gain importance.

Mexico is a country with substantial economic disparities; however, it is classified as a middle-income country (MIC) by the World Bank [1], Mexico has a population of 130,861,007 inhabitants (2024) [2], with an incidence of cancer estimated at 16 cases per 100,000 in individuals between 0-19 years, with leukemia being the most frequent malignancy. Leukemia incidence and mortality rates are 5.5 and 2.5 cases per 100,000, respectively, exceeding global averages by approximately 1.8-fold and twofold [3]. Mexican pediatric patients with ALL differs from other populations towards higher risk, have increased rates of relapse, treatment related toxicity and treatment abandonment [4]. Notably, 54% of patients are classified as high risk at diagnosis and exhibit poor responses to treatment, with early relapses occurring at least three times more frequently within the first 18 months of therapy [5]. In the genetic background, a lower incidence of t(12,21)(ETV6::RUNX1) associated with standard prognosis is reduced, conversely, rearrangements of CRLF2 and iAMP21, which confer high risk are widely represented in the Mexican pediatric patients [4].

4. Results: ...Among received samples only 45 (1.3%) were non-evaluable for result interpretation, in which case the pediatric hematologist or oncologist was notified, and a new BM aspiration was requested for processing. .... add some comment and give more details for this high rate. DR

We thank the reviewer for the comment and the opportunity to expand on this issue. The OCL is located in a rural and decentralized area of the capital of Mexico, which enables alignment with country healthcare needs, where the recommendation is that samples be transported rather than patients. During the implementation of the laboratory, the main challenges have been logistics and courier services required to ensure timely delivery of samples and preserve their viability.

In 2022, samples were received from 18 states, with a non‑evaluable sample rate of 1.8%. This relatively high rate was due to the establishment of a new routing system that involved decentralization and transport to a newly created OCL. In 2023, the number of samples received increased by 130% and the proportion of non‑evaluable samples fell to 0.3%, reflecting a collective effort to reinforce guidelines for sample collection, handling, and shipment to ensure viability.

In 2024 the sample volume raises substantially compared with 2022 and 2023 (268% and 60%, respectively), which was accompanied by an increase in non‑evaluable samples (2.0%) due to transport from six additional states. In response, communication with clinical coordinators and carriers was strengthened to optimize collection, transport, and delivery to the OCL.

This adjustment had a marked effect in 2025: although sample volume increased by 21% in comparison to 2024, the proportion of non‑evaluable samples decreased to 0.7% of the total received during the study period, while maintaining submissions from 26 states. This highlights the importance of clear communication among all stakeholders to ensure process reliability and avoid repeated sampling in vulnerable patients.

Across the study, the average transport time for non‑evaluable samples was 2.3 days (2022–2025), whereas a representative set of assessable samples showed an estimated transport time of 1.3 days to OCL. These additional 24 hours are critical to preserve cellular viability and enable accurate interpretation.

OCL’s efforts and efficient communication with OncoCREAN centers remain focused on reducing the proportion of non‑assessable samples.

5. Discussion: it is weak compared to other sections; please expand it with more studies and discuss in detail

We expanded the discussion section based on the reviewer's valuable suggestion. We are very grateful to the reviewer.

References

1. Zapata-Tarrés, M.; Velasco-Hidalgo, L.; González-Garay, A.; Cárdenas-Cardos, R.; Rivera-Luna, R. Does the Human Development Index Relate with Acute Lymphoblastic Leukemia Incidence? Bol. Med. Hosp. Infant. Mex. 2021, 78, 301–305, doi:10.24875/BMHIM.20000043.
2. Perfil de País - Mexico | Salud En Las Américas Available online: https://hia.paho.org/es/perfiles-de-pais/mexico (accessed on 14 January 2026).
3. Cancer Today Available online: https://gco.iarc.fr/today/en/dataviz/bars?mode=population&key=crude_rate&age_end=3&populations=484_900&types=0_1&sort_by=value1&cancers=36 (accessed on 14 January 2026).
4. Rivera-Luna, R.; Perez-Vera, P.; Galvan-Diaz, C.; Velasco-Hidalgo, L.; Olaya-Vargas, A.; Cardenas-Cardos, R.; Aguilar-Ortiz, M.; Ponce-Cruz, J. Triple-Hit Explanation for the Worse Prognosis of Pediatric Acute Lymphoblastic Leukemia among Mexican and Hispanic Children. Front. Oncol. 2022, 12, doi:10.3389/FONC.2022.1072811.
5. Zapata-Tarrés, M.; Balandrán, J.C.; Rivera-Luna, R.; Pelayo, R. Childhood Acute Leukemias in Developing Nations: Successes and Challenges. Curr. Oncol. Rep. 2021, 23, doi:10.1007/S11912-021-01043-9.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Accept in the current form

Reviewer 2 Report

Comments and Suggestions for Authors

good work! discussion section is very well established and looks better now