Fortis Memorial Research Institute

Primary central nervous system (CNS) neuroblastoma, FOXR2-activated (CNS-NB-FOXR2), is a rare embryonal tumor characterized by neuroblastic differentiation and structural rearrangement of the FOXR2 gene. Previously grouped under CNS primitive neuroectodermal tumors (PNET), this entity has been reclassified based on genome-wide DNA methylation profiling. In this study, we present the detailed clinicopathological and immunohistochemical features of two pediatric cases diagnosed at our center. The first case involved a six-year-old with a left frontal mass; the second was a one-year-old with a large bifrontal lesion. Radiologically, both cases mimicked other embryonal tumors or high-grade gliomas. Histologically, tumors displayed small round blue cell morphology with neuroblastic features, including Homer-Wright rosettes and ganglionic differentiation. Immunohistochemistry demonstrated diffuse positivity for OLIG2, synaptophysin, and L1CAM, with negative expression for GFAP, EMA, and IDH1 R132H. FOXR2 showed nuclear positivity in both cases, supporting the diagnosis. Both cases exhibited diffuse L1CAM positivity-a rare finding with limited evidence in the existing literature. Although DNA methylation profiling could not be performed, the diagnosis was supported by characteristic morphology and immunohistochemistry profile. This report highlights key diagnostic features and potential mimics of CNS neuroblastoma, FOXR2-activated, and underscores the utility of immunohistochemistry in low-resource settings. Recognizing this entity is essential for accurate classification and appropriate therapeutic planning. Further studies are warranted to explore targeted therapies, including MEK inhibitors, which may hold promise based on emerging molecular data.

Blast crisis in chronic myeloid leukemia (CML) with metachromatic granules poses a diagnostic challenge. We report two CML patients who defaulted therapy and presented in blast crisis (BC) with numerous blasts showing metachromatic granules. Flowcytometric immunophenotyping and immunohistochemistry aided in identifying immature mast cells (MC), while the absence of the KITD816V mutation helped to narrow down and support the diagnosis of myeloid BC with MC differentiation or myelomastocytic leukemia (MML). These cases emphasize the diagnostic difficulty when MML occurs in BC and highlight the need for further research and refined classification of these rare hematologic entities.

Abstract Purpose Diffuse midline glioma (DMG) and midline glioblastoma (mGBM) are aggressive WHO grade 4 tumors with comparable median survival of 12–18 months but require fundamentally different therapeutic approaches. Despite their clinical urgency, non-invasive differentiation remains challenging due to overlapping conventional MRI features and the difficulty of obtaining tissue diagnosis from eloquent midline locations. Methods This retrospective study included 62 patients with histologically confirmed midline gliomas (30 mGBM, 32 DMG) evaluated with 3T MRI. Quantitative DCE-MRI perfusion parameters (rCBV, rCBF, Slope-2, K trans , V p , V e ) were computed and compared between the midline tumor types. Statistical analyses included Shapiro-Wilk test, t-test, and ROC curve analysis using perfusion parameters. Machine learning-based classification was also performed using four classifiers and 5-fold cross-validation, evaluating all possible feature combinations among the best features from the perfusion parameters. Results The 95th percentile values of perfusion parameters demonstrated superior discriminative capability between mGBM and DMG. DMG exhibited significantly lower perfusion parameter values compared to mGBM (p < 0.05). Individual perfusion parameters, particularly rCBF, rCBV, V e showed discriminative performance achieving AUC values ranging from 70.62% to 75.31%, for differentiating mGBM vs DMG. Machine learning classifiers used these features for evaluating 7 combinations. Three parameter combination (rCBV + rCBF + V e ) using RF achieved highest cross-validation accuracy (76.67 ± 7.16%) with consistent sensitivity (80.00%) across all models. Conclusions Quantitative DCE-MRI perfusion analysis provides significant diagnostic value for differentiating DMG from mGBM, offering a non-invasive alternative when tissue diagnosis is not obtained. Both individual parameters and optimized multi-parametric approaches demonstrate clinically useful performance for guiding treatment decisions.

Colorectal cancer (CRC) is one of the most molecularly heterogeneous malignancies, with complexity that extends far beyond traditional histopathological classifications. The consensus molecular subtypes (CMS) established in 2015 brought a marked advancement in the taxonomy of CRC, consolidating six classification systems into four novel subtypes, which focus on vital gene expression patterns and clinical and prognostic outcomes. However, nearly a decade of clinical experience with CMS classification has revealed fundamental limitations that underscore the inadequacy of any single classification system for capturing the full spectrum of CRC biology. The inherent challenges of the current paradigm are multifaceted. In the CMS classification, mixed phenotypes that remain unclassifiable constitute 13% of CRC cases. This reflects the remarkable heterogeneity that CRC shows. The tumor budding regions reflect the molecular shift due to CMS 2 to CMS 4 switching, causing further heterogeneity. Moreover, the reliance on bulk RNA sequencing fails to capture the spatial organization of molecular signatures within tumors and the critical contributions of the tumor microenvironment. Recent technological advances in spatial transcriptomics, single-cell RNA sequencing, and multi-omic integration have revealed the limitations of transcriptome-only classifications. The emergence of CRC intrinsic subtypes that attempt to remove microenvironmental contributions, pathway-derived subtypes, and stem cell-based classifications demonstrates the field’s recognition that multiple complementary classification systems are necessary. These newer molecular subtypes are not discrete categories but biological continua, thus highlighting that the vast molecular landscape is a tapestry of interlinked features, not rigid subtypes. Multiple technical hurdles cause difficulty in implementing the clinical translation of these newer molecular subtypes, including gene signature complexity, platform-dependent variations, and the difficulty of getting and preserving fresh frozen tissue. CMS 4 shows a poor prognostic outcome among the CMS subtypes, while CMS 1 is associated with poor survival in metastatic cases. However, the predictive value for definitive therapy remains subdued. Looking forward, the integration of artificial intelligence, liquid biopsy approaches, and real-time molecular monitoring promises to enable dynamic, multi-dimensional tumor characterization. The temporal and spatial complexity can only be captured by complementary molecular taxonomies rather than a single, unified system of CRC classification. Such an approach recognizes that different clinical questions – prognosis, treatment selection, resistance prediction – may require different molecular lenses, each optimized for specific clinical applications. This editorial advocates for a revolutionary change from pursuing a single “best” classification system toward a diverse approach that welcomes the molecular mosaic of CRC. Only through such comprehensive molecular characterization can we hope to achieve the promise of precision oncology for the diverse spectrum of patients with CRC.

Biliary tract cancers (BTCs), including gallbladder cancer (GBC) and cholangiocarcinoma (CCA), are rare but aggressive malignancies with distinct molecular landscapes and poor prognoses. Genomic profiling has revealed significant molecular alterations, but the genomic landscape of BTC in the Indian population remains underexplored. This study aims to comprehensively characterize the mutation landscape of BTC in the Indian population. A total of 154 BTC cases, including 69 CCA and 85 GBC, were retrospectively analyzed using data collected from various targeted sequencing panels. Somatic mutations, copy number variations (CNVs), and gene fusions in key oncogenic and tumor suppressor genes were identified from these panel reports. Downstream analyses were performed to derive key biological insights, including pathway enrichment and mutual exclusivity and co-occurrence analyses of genomic alterations. TP53 was the most frequently mutated gene (53%), followed by KRAS (18%), ARID1A (9%), IDH1 (7%), and PIK3CA (7%). Recurrent amplifications were observed in MYC (12%) and ERBB2 (9%). Pathway enrichment analysis revealed significant dysregulation in the PI3K-AKT-mTOR, Notch, and Wnt/β-catenin signaling pathways. Notably, IDH1 mutations were primarily observed in CCA, while STK11 mutations were exclusive to GBC, highlighting distinct molecular characteristics between the two subtypes. PD-L1-negative tumors exhibited distinct genomic alterations, notably SMAD4 mutations, which were associated with reduced PD-L1 expression. This loss of SMAD4, involved in TGF-β signaling, could impair immune response regulation and facilitate immune evasion. This study provides a comprehensive molecular profiling of BTCs in the Indian population, revealing key genomic alterations, subtype-specific differences, and associations with immune features. The findings underscore the importance of molecular profiling in guiding personalized treatment strategies.

In this study, we present a neural network (NN) based recommender system with novel custom loss function developed to recommend fee for its pricelock product.It is a popular add-on product that allows users to lock a flight price and book it later at the same locked price, even if the price increases while flight booking.The core challenge in enabling this product lies in predicting the magnitude of future price changes over time horizons.We formulate this problem as a multi-task learning (MTL) setup, where price change magnitudes are modeled as ordinal categories across several time intervals modeled as heads.Crucially, we address the ordinal nature of price change buckets by introducing a novel loss function called Learnable Soft Ordinal Regression (L-SORD).Our demo showcases how this system improves both predictive accuracy and revenue performance, enabling more effective price recommendations in a high stakes, real world environment.This work highlights the potential of combining MTL architectures with custom loss functions in production grade pricing recommender systems.

Sickle cell disease (SCD) is the most common hemoglobinopathy, affecting approximately 300,000 newborns worldwide each year. Hematopoietic stem cell transplantation (HSCT) is the only current curative option for the disease. Still, it is hindered by the availability of suitable donors, socio-economic issues, transplant failure and long-term complications of transplant, including graft-versus-host disease—acute and chronic. To date, there is no standardized protocol for conditioning regimens in SCD patients. A total of 100 pediatric patients diagnosed with sickle cell disease (SCD) underwent allogeneic hematopoietic stem cell transplantation (HSCT) between January 2015 and December 2024. Fifty-five patients (59.8%) underwent HLA-identical sibling-donor, and 37(40.2%) underwent haploidentical transplants. Eighty-three (91.2%) had stable engraftment. The median follow-up time was 31.6 months. Overall survival was 86.9%(95% CI: 79.3%–93.4%) in our cohort of 92 patients transplanted for SCD from either HLA-matched siblings or haploidentical donors, with a median follow-up of 53 months, with EFS of 77% without death or rejection. The survival rates were significantly higher in MSD HSCT (53/55, 96.4% vs 27/37, 78.3%, p < 0.01). The outcomes in haploidentical outcomes have significantly improved (2014–2018 vs 2019 to 2023). The cumulative incidence of acute graft-versus-host-disease (GVHD) was 26% (95% CI − 17.9% to 36.8%) and of chronic GVHD was 8.4% (95% CI − 3.7% to 17.1%) at 2-year post-transplant. Viral reactivations were seen in 18 patients. In haploidentical transplants, we gradually drifted toward reduced toxicity conditioning, including Thio-Flu-Cy-TBI-ATG and found better outcomes through the years. The use of post-transplantation (PTCy) has led to a significantly reduced risk of GVHD.