During 2012–2013, at a public hospital in Pune, India, 26 (3.9%) cases of tuberculosis were reported among 662 medical trainees, representing an estimated incidence of 3,279 cases/100,000 person-years. Three of these infections were isoniazid-resistant, 1 was multidrug-resistant, and 1 occurred in a trainee who had fulminant hepatitis after starting treatment for TB.

Chronic myeloid leukemia (CML) patients with complex chromosomal translocations as well as non-compliant CML patients often demonstrate short-lived responses and poor outcomes on the current therapeutic regimes using Imatinib and its variants. It has been derived so far that leukemic stem cells (LSCs) are responsible for Imatinib resistance and CML progression. Sonic hedgehog (Shh) signaling has been implicated in proliferation of this Imatinib-resistant CD34(+) LSCs. Our work here identifies the molecular mechanism of Shh-mediated mutation-independent Imatinib resistance that is most relevant for treating CML-variants and non-compliant patients. Our results elucidate that while Shh can impart stemness, it also upregulates expression of anti-apoptotic protein—Bcl2. It is the upregulation of Bcl2 that is involved in conferring Imatinib resistance to the CD34(+) LSCs. Sub-toxic doses of Bcl2 inhibitor or Shh inhibitor (<<IC50), when used as adjuvants along with Imatinib, can re-sensitize Shh signaling cells to Imatinib. Our work here highlights the need to molecularly stratify CML patients and implement combinatorial therapy to overcome the current limitations and improve outcomes in CML.

BACKGROUND : Clinical Phenotype and outcomes of patients with Acute myeloid leukemia (AML) in the Indian subcontinent differs from published literature. A younger age at diagnosis and higher induction mortality complicate AML management in India(1). Metaphase Karyotyping represents the backbone of prognostication and risk stratification in AML. Optimal treatment strategies for the cohort of Cytogenetically normal AML are still under evaluation. Applications of Next generation Sequencing (NGS) techniques in AML have unravelled the genetic heterogeneity of this disease. Whole genome sequencing has identified many novel mutations leading to tremendous improvements in diagnosis and risk stratification. Development of therapies targeting these genetic alterations is enabling a gradual shift from non-specific approaches to personalised therapy tailored to an individual patient's genome. This will undoubtedly translate to better clinical outcomes for this disease, with otherwise poor prognosis. Whole genome sequencing is still in a nascent stage in Indian settings with no published literature on genomics in AML till date. We aimed to study the genomic landscape of AML in the Indian population and to co-relate this with clinical outcomes over the course of 1 year. METHODS: We recruited 34 newly diagnosed patients with AML who presented to our Centre (Mazumdar Shaw Medical Centre, Narayana Health City, Bangalore, India) between November 2017 and May 2018. Clinical and laboratory details of all patients were recorded. Bone marrow and paired peripheral blood samples were drawn before initiating therapy. Whole genome sequencing and Exome capture was done for each sample using Ilumina HiSeq platform. Patients were risk stratified as per ELN 2017 and treated as per NCCN guidelines. Patients were followed up prospectively for one year from initial diagnosis. Genetic results were stratified according to gene function and analysed with respect to predefined clinical outcomes (remission status post induction, relapse rates, progression free and overall survival). RESULTS: Amongst the 34 study participants, 5 patients failed QC during sequencing and were de-recruited. Hence 29 patients were available for final analysis. Median age of patients was 42 years with 13 patients (44.8%) less than 40 years of age.18 patients (60%) had normal cytogenetics at baseline.17 patients (58%) were classified as intermediate risk and 6 patients each as Standard and high risk, as per ELN 2017. 22 patients (79.3%) patients received standard Induction chemotherapy (3+7 regimen) while 6 patients received hypomethylating agents. Overall CR rate following induction at Day 28 was 50% and Induction mortality was 21.42%. 6 patients underwent an Allogenic Stem cell transplant. A total of 96 mutations (47 driver and 49 VUS mutations) in 123 genes were identified. The average number of Driver mutations was 1.48 per patient. IDH genes were the most frequently mutated Driver genes followed by FLT3 mutations. Frequency of NPM1 mutations was significantly low (17.25%). Highest frequency of VUS mutations was seen in the ETV6, ATM and CBLC genes. Highest frequency of somatic mutations were identified in the genes encoding for myeloid transcription factors and DNA methylation. Average driver mutations showed significant co-relation to Age (> 60 years) and high burden of Bone marrow blasts (>30%). An updated risk stratification incorporating mutation analysis findings resulted in re-stratification of 8 intermediate risk patients into high risk. 2 patients with detectable FLT3 ITD mutation by NGS were negative by PCR. Choice of consolidation therapy and Driver mutation status were found to show statistically significant association with both Event free survival and Overall survival at 1 year. Increased driver mutation burden was associated with increased refractoriness to chemotherapy and poor EFS and OS. Mutations in Tumour suppressor genes, were associated with suboptimal treatment outcomes and poor survival. CONCLUSIONS Genomic landscape of AML in Indian patients shows significant differences from published literature. This may hold clues to the differing biological characteristics of AML seen in this population. Genome based risk stratification and tailored therapy needs to be adapted into the management of AML. This data provides valuable insights into developing therapeutic strategies for Indian patients. Disclosures No relevant conflicts of interest to declare.

Plasmablastic neoplasms comprise various haematolymphoid tumours with plasmablastic morphology which includes Plasmablastic Myeloma (PBM) and Plasmablastic Lymphoma (PBL). Distinguishing these two entities remains a major diagnostic challenge. In view of Epstein Barr Virus (EBV)-Encoded RNA (EBER) negativity, Human Immunodeficiency Virus (HIV) negativity, high Serum Free Light Chain (SFLC) assay and absence of hypermetabolic lymphadenopathy, a final diagnosis of PBM was made. This report is about a 55-year-old lady who presented with fatigue, significant loss of weight, and appetite. She had mild enlargement of the liver, spleen and no significant lymphadenopathy. There were atypical cells in peripheral blood. Bone marrow evaluation showed 51% atypical mononuclear cells. Flow cytometry was negative for acute leukaemia diagnostic markers. Immunohistochemistry (IHC) on the bone marrow biopsy revealed positivity for Cluster of Differentiation (CD) 138, Multiple Myeloma 1 (MUM1) with kappa light chain restriction and negative for EBER. The free light chain showed a kappa:lambda light chain ratio of 28,885 (0.26-1.65). The diagnosis of PBM was made and she was started on a daratumumab-based immunotherapy regimen. She achieved complete remission after induction with Measurable Residual Disease (MRD) <0.01%. She is presently doing well on follow-up with the disease in remission status.

INTRODUCTION Acute Graft-versus-host disease (aGVHD) is a common complication of allogeneic hematopoietic cell transplantation (HCT), affecting about 50% of transplants. Grading of aGVHD can serve a variety of purposes, including retrospective assessment of peak severity, real-time assessment of severity at prespecified time points, and determination of the need for treatment. But several problems hamper the application of grading systems to predict outcomes among patients with aGVHD: (1) Assignment of a peak GVHD score is done retrospectively; clinicians cannot use the current grading system for peak score in real-time1. (2) The systems do not account for the time to the response after treatment1. (3) Assignment of grade IV GVHD is often used to indicate that GVHD caused a death, irrespective of the severity. In this situation, the grading reflects the outcome and cannot be used to predict the outcome1. Recently serum biomarkers have emerged as an additional potential measurement of acute GVHD severity. The Mount Sinai Acute GVHD International Consortium (MAGIC) Group, has validated MAGIC algorithm probability (MAP) that combines two GI biomarkers (ST2 and REG3α) into a single value. The MAP predicts response to treatment, GVHD severity. But in resource-limited settings, like transplant centers in India lack testing features. In this study we have developed a risk scoring based on clinical and easily available biochemical parameters to predict the severity of aGVHD. AIMS AND OBJECTIVES To predict the aGVHD severity at the onset based on risk factor score. To assess the steroid response in different risk groups MATERIAL AND METHODS The study included patients who underwent allogeneic HCT at Narayana hrudayalaya hospital, between January 2015 and April 2020 and developed acute GVHD within 100 days of transplant. After taking institutional ethics committee approval, data were collected from medical records. Baseline patient characteristics are mentioned in table 1. The following parameters were analyzed as risk factors for the development of severe GVHD (MAGIC grade 3 and 4): 1. Age &gt;18 yrs, 2. MDR organisms in baseline stool culture, 3. HCT comorbidity index &gt;1, 4. Peripheral blood as a source of stem cells, 5. Female to male transplants, 6. Myeloablative regimens, 7. Suboptimal GVHD prophylaxis, 8. CD34 dose &gt; 6 x 106/kg, 9. Grade 3/4 mucositis 10. Early-onset GVHD (within 28 days), 11. Albumin level at the onset of GVHD, 12. Albumin drop from baseline3, and 13. Bloodstream infection. Risk factors with a p-value of &lt;0.05, were given score 2 and score 1 was given to other parameters. The total score ranged from 0 to 17. The study population was divided into 3 groups (Group 1 with score 0-4, Group 2 with score 5-7, Group 3 with score &gt; 8). The following outcomes were assessed in each group; severity of GVHD (Grade I/II vs Grade III/IV) and response to steroids. RESULTS Out of 148 patients, 35.5% of Group 1, 56.5% of Group 2 patients and 85.5% of Group 3 patients developed Grade 3 or 4 GVHD respectively (P-value &lt;0.001). Positive predictive value of score &gt; 8 to predict Grade 3/4 GVHD is 85.4%, negative predictive value is 50.6%, sensitivity is 50.5%, and specificity is 85.5%. From Group 1 and 2, only 30% of patients were steroid non responders, while 55.3% of Group 3 patients are steroid non responders (P-value - 0.04). CONCLUSION Traditional GVHD scoring systems reflect the outcome and cannot be used to predict the outcome. Various biomarker-based scoring systems are helpful in this situation, but in resource-limited settings, it might not be easily feasible. Clinical scoring systems like risk factor-based scoring systems are very helpful, which can predict the severe GVHD at early time points leading to management decisions such as upfront initiation of aggressive treatments and earlier introduction of second-line agents. References Leisenring WM, et al. An acute graft-versus-host disease activity index to predict survival after hematopoietic cell transplantation with myeloablative conditioning regimens. Blood. 2006;108(2):749-55. 2.Rashidi A, et al. Peritransplant Serum Albumin Decline Predicts Subsequent Severe Acute Graft-versus-Host Disease after Mucotoxic Myeloablative Conditioning. Biol Blood Marrow Transplant. 2016;22(6):1137-41. Disclosures No relevant conflicts of interest to declare.