Splenectomy outcomes in immune cytopenias: Treatment outcomes and determinants of response.

BACKGROUND: Splenectomy is commonly used to treat refractory immune-mediated cytopenia, but there are no established factors that are associated with response to the procedure. OBJECTIVES: A cohort study was conducted to evaluate the hematologic and surgical outcomes of splenectomy in adult patients with immune cytopenias and identify preoperative factors associated with response. METHODS: Data from the Cleveland Clinic Foundation for 1824 patients aged over 18 who underwent splenectomy from 2002 to 2020 were analyzed. RESULTS: The study found that the most common indications for splenectomy were immune thrombocytopenic purpura (ITP) and autoimmune hemolytic anemia, with a median age of 55 years and median time from diagnosis to splenectomy of 11 months. Hematologic response rates were 74% overall, with relapse in 12% of cases. Postsplenectomy discordant diagnoses were present in 13% of patients, associated with higher relapse rates. Surgery-related complications occurred in 12% of cases, whereas only 3% of patients died from disease complications. On univariate analysis, preoperative factors associated with splenectomy treatment failure were ≥3 lines of pharmacologic treatment, whereas isolated thrombocytopenia, primary ITP, and age ≤40 years had a strong association with response. The multivariable regression confirmed that treatment failure with multiple lines of medical therapy was associated with the failure to respond to splenectomy. CONCLUSION: Overall, the study demonstrates that splenectomy is an effective treatment option for immune-mediated cytopenias with a low complication rate.

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia.

BACKGROUND: TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. METHODS: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. RESULTS: Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. CONCLUSION: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Targeting HIF-2 Alpha in Renal Cell Carcinoma.

OPINION STATEMENT: Current treatment options for patients with metastatic renal cell carcinoma (mRCC) are limited to immunotherapy with checkpoint inhibitors and targeted therapies that inhibit the vascular endothelial growth factor receptors (VEFG-R) and the mammalian target of rapamycin (mTOR). Despite significantly improved outcomes over the last few decades, most patients with mRCC will ultimately develop resistance to these therapies, thus highlighting the critical need for novel treatment options. As part of the VHL-HIF-VEGF axis that rests at the foundation of RCC pathogenesis, hypoxia-inducible factor 2α (HIF-2α) has been identified as a rationale target for mRCC treatment. Indeed, one such agent (belzutifan) is already approved for VHL-associated RCC and other VHL-associated neoplasms. Early trials of belzutifan indicate encouraging efficacy and good tolerability in sporadic mRCC as well. The potential inclusion of belzutifan and other HIF-2α inhibitors into the mRCC treatment armamentarium either as a single agent or as combination therapy would be a welcome addition for patients with mRCC.

Molecular patterns identify distinct subclasses of myeloid neoplasia.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia.

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Endocrine Toxicity and Outcomes in Patients With Metastatic Malignancies Treated With Immune Checkpoint Inhibitors.

CONTEXT: Immune checkpoint inhibitors (ICIs) have gained a revolutionary role in management of many advanced malignancies. However, immune-related endocrine events (irEEs), have been associated with their use. irEEs have nonspecific clinical presentations and variable timelines, making their early diagnosis challenging. OBJECTIVE: To identify risk factors, timelines, and prognosis associated with irEEs development. DESIGN AND SETTING: Retrospective observational study within the Cleveland Clinic center. PATIENTS: Metastatic cancer adult patients who received ICIs were included. METHODS: 570 charts were reviewed to obtain information on demographics, ICIs used, endocrine toxicities, cancer response to treatment with ICI, and overall survival. MAIN OUTCOME MEASURES: Incidence of irEEs, time to irEEs development and overall survival of patients who develop irEEs. RESULTS: The final cohort included 551 patients. The median time for the diagnosis of irEEs was 9 weeks. Melanoma was associated with the highest risk for irEEs (31.3%). Ipilimumab appeared to have the highest percentage of irEEs (29.4%), including the highest risk of pituitary insufficiency (11.7%), the most severe (Grade 4 in 60%) and irreversible (100%) forms of irEEs. Forty-five percent of patients with irEEs had adequate cancer response to ICI compared to 28.3% of patients without irEEs (P = 0.002). Patients with irEEs had significantly better survival compared to patients without irEEs (P < 0.001). CONCLUSIONS: In the adult population with metastatic cancer receiving treatment with ICI, irEEs development may predict tumor response to immunotherapy and a favorable prognosis. Ipilimumab use, combination ICI therapy, and melanoma are associated with a higher incidence of irEEs.

Validation of the IMPEDE VTE score for prediction of venous thromboembolism in multiple myeloma: a retrospective cohort study.

The IMPEDE VTE score has recently emerged as a novel risk prediction tool for venous thromboembolism (VTE) in multiple myeloma (MM). We retrospectively reviewed 839 patients with newly diagnosed MM between 2010 and 2015 at Cleveland Clinic and included 575 patients in final analysis to validate this score. The c-statistic of the IMPEDE VTE score to predict VTE within 6 months of treatment start was 0·68 (95% CI: 0·61-0·75). The 6-month cumulative incidence of VTE was 5·0% (95% CI: 2·1-7·9) in the low risk group, compared to 12·6% (95% CI: 8·9-16·4%) and 24·1% (95% CI: 12·2-36·1) in the intermediate and high risk groups (P < 0·001 for both). In addition, a higher proportion of patients in the VTE cohort had ECOG performance status of ≥2 as compared to the no VTE cohort (33% vs. 16%, P = 0·001). Other MM characteristics such as stage, immunoglobulin subtype, and cytogenetics were not predictors of VTE. In summary, we have validated the IMPEDE VTE score in our patient cohort and our findings suggest that it can be utilized as a VTE risk stratification tool in prospective studies looking into investigating VTE prophylaxis strategies in MM patients.

Clinical Characteristics and Outcomes of Primary Breast Lymphoma: The Cleveland Clinic Experience.

Introduction Primary breast lymphoma (PBL) is a rare malignancy that accounts for less than 0.5% of all breast malignancies. Materials and Methods We retrospectively analyzed 36 PBL patients to report the clinical characteristics and outcomes of patients with indolent and aggressive histologic subtypes. Results Thirteen (36%) patients had aggressive and 23 (64%) had indolent PBL. Marginal zone lymphoma was the most common histologic subtype (33%). Stage IE, IIE, and IV disease were seen in 27 (75%), six (17%), and three (8%) patients, respectively. Patients with aggressive PBL more often presented with a breast lump and/or B symptoms (unexplained weight loss, fever, night sweats) (78% vs. 31%, p = 0.005). Commonly used treatment modalities for aggressive vs. indolent PBL were chemotherapy alone (23% vs. 26%, p = 0.8), chemoradiotherapy (46% vs. 9%, p = 0.009), radiotherapy alone (15% vs. 22%, p = 0.6), and observation (0% vs. 26%, p = 0.07), respectively. The five-year overall survival (OS) and progression-free survival (PFS) of PBL patients were 82% (95% CI: 67 - 100) and 63% (95% CI: 45 - 89), respectively. The five-year OS of patients with aggressive vs. indolent PBL were 92% (95% CI: 77 - 100) vs. 80% (95% CI: 63 - 100), respectively (p = 0.6). The five-year OS of patients who received > 1, 1, and 0 treatment modalities were 92% (95% CI: 77 - 100), 86% (95% CI: 63 - 100), and 53% (95% CI: 21 - 100), respectively. Conclusion In our cohort, the higher utilization of chemoradiotherapy in aggressive PBL was able to overcome the worse prognosis of these patients. At least one treatment modality should be considered in patients with indolent PBL, given that observation alone was associated with a poor prognosis.

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.

Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.