Blood
DLBclass: A Probabilistic Molecular Classifier to Guide Clinical Investigation and Practice in DLBCL
Chapuy B, Wood TR, Stewart C, Dunford AJ, Wienand K, Khan SJ, Calabretta E, Van Seters S, Wiseman S, Belkin S, Heiman DI, Redd RA, Shipp MA, Getz G
Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous disease. The increasing recognition and targeting of genetically defined DLBCLs highlights the need for robust classification algorithms. We previously characterized recurrent genetic alterations in DLBCL and identified five discrete subtypes, Clusters 1-5 (C1-C5), with unique mechanisms of transformation, immune evasion, candidate treatment targets and different outcomes following standard first-line therapy. Herein, we validate the C1-C5 DLBCL taxonomy in an independent dataset and use the expanded series of 699 primary DLBCLs to develop a probabilistic molecular classifier and confirm its performance in an independent test set. Using our previously assigned cluster labels as a reference, we systematically compared multiple machine learning models and strategies for input feature dimensionality reduction with a newly developed performance metric that captured the relationship between accuracy and confidence of class assignments. The winning neural network model, DLBclass, assigned all cases in the training/validation and independent test sets with 91% and 89% accuracies, respectively. In the 75% of cases with confidence >0.7, DLBclass assignments were accurate in 97% of the training/validation set and 98% of the test set. DLBclass enables robust prospective classification of single cases for inclusion in genetically guided clinical trials or practice and represents a framework for the development of genomic-based classification methods in other cancers.
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Blood
How I Treat AML Relapse After Allogeneic HCT
Gooptu M, Murdock HM, Soiffer RJ
Allogeneic hematopoietic stem-cell transplantation (HCT) is one of the principal curative approaches in the treatment of acute myeloid leukemia (AML); however, relapse post-transplantation remains a catastrophic event with poor prognosis. The incidence of relapse has remained unchanged over the last three decades despite an evolving understanding of the immunobiology of the graft-versus leukemia effect and the immune escape mechanisms that lead to post-HCT relapse. The approach to post-transplant relapse is highly individualized and is dictated both by disease biology and genomics as well as the patient's clinical status at the time of relapse and the interval between relapse and transplantation. With the help of three illustrative cases, we discuss our approach to early, late, and incipient relapse. Current therapeutic strategies incorporate immunosuppression taper when feasible, a variety of targeted and non-targeted chemotherapeutic agents and consolidative cellular therapies including donor lymphocyte infusions or a second allogeneic transplant. We then summarize evolving frontiers in the treatment and prognostication of relapse including the critical role of measurable residual disease. Finally, we emphasize enrollment on clinical trials and thoughtful discussions regarding goals of care and supporting frail patients as universal principles that should be incorporated in approaches to treatment of AML relapse post-transplantation.
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Blood
Sustained Benefit of Zanubrutinib vs Ibrutinib in Patients with R/R CLL/SLL: Final Comparative Analysis of ALPINE
Brown JR
The ALPINE trial established the superiority of zanubrutinib over ibrutinib in patients with relapsed/refractory chronic lymphocytic leukemia and small lymphocytic lymphoma; here, we present data from the final comparative analysis with extended follow-up. Overall, 652 patients received zanubrutinib (n = 327) or ibrutinib (n = 325). At an overall median follow-up of 42.5 months, progression-free survival benefit with zanubrutinib vs ibrutinib was sustained (hazard ratio [HR], 0.68; 95% confidence interval [CI], 0.54-0.84), including in patients with del(17p)/TP53 mutation (HR, 0.51; 95% CI, 0.33-0.78) and across multiple sensitivity analyses. Overall response rate remained higher with zanubrutinib compared with ibrutinib (85.6% vs 75.4%); responses deepened over time with complete response/complete response with incomplete bone marrow recovery rates of 11.6% (zanubrutinib) and 7.7% (ibrutinib). Although median overall survival has not been reached in either treatment group, fewer zanubrutinib patients have died than ibrutinib patients (HR, 0.77 [95% CI, 0.55-1.06]). With median exposure time of 41.2 and 37.8 months in zanubrutinib and ibrutinib arms, respectively, the most common nonhematologic adverse events included COVID-19-related infection (46.0% vs 33.3%), diarrhea (18.8% vs 25.6%), upper respiratory tract infection (29.3% vs 19.8%), and hypertension (27.2% vs 25.3%). Cardiac events were lower with zanubrutinib (25.9% vs 35.5%) despite similar rates of hypertension. Incidence of atrial fibrillation/flutter was lower with zanubrutinib vs ibrutinib (7.1% vs 17.0%); no cardiac deaths were reported with zanubrutinib vs 6 cardiac deaths with ibrutinib. This analysis, at 42.5 months median follow-up, demonstrates that zanubrutinib remains more efficacious than ibrutinib with an improved overall safety/tolerability profile. This trial was registered at www.ClinicalTrials.gov as #NCT03734016.
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Cancer Cell
Cytokines in Cancer
Kureshi CT, Dougan SK
Cytokines are proteins used by immune cells to communicate with each other and with cells in their environment. The pleiotropic effects of cytokine networks are determined by which cells express cytokines and which cells express cytokine receptors, with downstream outcomes that can differ based on cell type and environmental cues. Certain cytokines, such as interferon (IFN)-?, have been clearly linked to anti-tumor immunity, while others, such as the innate inflammatory cytokines, promote oncogenesis. Here we provide an overview of the functional roles of cytokines in the tumor microenvironment. Although we have a sophisticated understanding of cytokine networks, therapeutically targeting cytokine pathways in cancer has been challenging. We discuss current progress in cytokine blockade, cytokine-based therapies, and engineered cytokine therapeutics as emerging cancer treatments of interest.
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Journal of Clinical Oncology
Hypofractionated Preoperative Radiation Should Not Yet Be Used as Standard of Care for Extremity and Truncal Soft Tissue Sarcoma
Baldini EH
Soft tissue sarcomas (STS) represent <1% of all cancers and occur anywhere in the body, with 75% arising in extremities and trunk. Landmark randomized trials for STS of extremities established the following: (1) combined limb-sparing surgery and radiation therapy (RT) is standard of care; (2) oncologic outcomes are similar with preoperative or postoperative RT, but toxicities differ. The American Society of Radiation Oncology, National Comprehensive Cancer Network, and European Society for Medical Oncology have endorsed (1) preference for preoperative RT over postoperative RT due to more favorable long-term toxicity profile and (2) conventional fractionation of 50 Gy in 25 fractions over 5 weeks as standard of care for preoperative RT for STS.
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JAMA Oncology
Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer
Rakaee M, Ricciuti B, Alessi JV, Adib E, Murilo Hidalgo Filho C, Di Federico A, Helland Å, Awad MM, Kwiatkowski DJ
IMPORTANCE: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.
OBJECTIVE: To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.
DESIGN, SETTING, AND PARTICIPANTS: This multicenter cohort study developed and independently validated a deep learning-based response stratification model for predicting ICI treatment outcome in patients with advanced NSCLC from whole slide hematoxylin and eosin-stained images. Images for model development and validation were obtained from 1 participating center in the US and 3 in the European Union (EU) from August 2014 to December 2022. Data analyses were performed from September 2022 to May 2024.
EXPOSURE: Monotherapy with ICIs.
MAIN OUTCOMES AND MEASURES: Model performance measured by clinical end points and objective response rate (ORR) differentiation power vs other predictive biomarkers, ie, programmed death-ligand 1 (PD-L1), tumor mutational burden (TMB), and tumor-infiltrating lymphocytes (TILs).
RESULTS: A total of 295 581 image tiles from 958 patients (mean [SD] age, 66.0 [10.6] years; 456 [48%] females and 502 [52%] males) treated with ICI for NSCLC were included in the analysis. The US-based development cohort consisted of 614 patients with median (IQR) follow-up time of 54.5 (38.2-68.1) months, and the EU-based validation cohort, 344 patients with 43.3 (27.4-53.9) months of follow-up. The ORR to ICI was 26% in the developmental cohort and 28% in the validation cohort. The deep learning model's area under the receiver operating characteristic curve (AUC) for ORR was 0.75 (95% CI, 0.64-0.85) in the internal test set and 0.66 (95% CI, 0.60-0.72) in the validation cohort. In a multivariable analysis, the deep learning model's score was an independent predictor of ICI response in the validation cohort for both progression-free (hazard ratio, 0.56; 95% CI, 0.42-0.76; P?<?.001) and overall survival (hazard ratio, 0.53; 95% CI, 0.39-0.73; P?<?.001). The tuned deep learning model achieved a higher AUC than TMB, TILs, and PD-L1 in the internal set; in the validation cohort, it was superior to TILs and comparable with PD-L1 (AUC, 0.67; 95% CI, 0.60-0.74), with a 10-percentage point improvement in specificity. In the validation cohort, combining the deep learning model with PD-L1 scores achieved an AUC of 0.70 (95% CI, 0.63-0.76), outperforming either marker alone, with a response rate of 51% compared to 41% for PD-L1 (?50%) alone.
CONCLUSIONS AND RELEVANCE: The findings of this cohort study demonstrate a strong and independent deep learning-based feature associated with ICI response in patients with NSCLC across various cohorts. Clinical use of this deep learning model could refine treatment precision and better identify patients who are likely to benefit from ICI for treatment of advanced NSCLC.
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Nature Communications
Cancer-Specific Innate and Adaptive Immune Rewiring Drives Resistance to PD-1 Blockade in Classic Hodgkin Lymphoma
Paczkowska J, Tang M, Wright KT, Song L, Luu K, Shanmugam V, Welsh EL, Weirather JL, Besson N, Olszewski H, Porter BA, Pfaff KL, Redd RA, Cader FZ, Mandato E, Ouyang J, Calabretta E, Bai G, Lawton LN, Armand P, Rodig SJ, Liu XS, Shipp MA
Hodgkin Reed-Sternberg (HRS) cells of classic Hodgkin lymphoma (cHL), like many solid tumors, elicit ineffective immune responses. However, patients with cHL are highly responsive to PD-1 blockade, which largely depends on HRS cell-specific retention of MHC class II and implicates CD4+ T cells and additional MHC class I-independent immune effectors. Here, we utilize single-cell RNA sequencing and spatial analysis to define shared circulating and microenvironmental features of the immune response to PD-1 blockade in cHL. Compared with non-responders, responding patients have more circulating CD4+ naïve and central memory T cells and B cells, as well as more diverse CD4+ T cell and B cell receptor repertoires. Importantly, a population of circulating and tumor-infiltrating IL1?+ monocytes/macrophages is detectable in patients with cHL but not healthy donors, and a proinflammatory, tumor-promoting signature of these circulating IL1?+ monocytes is associated with resistance to PD-1 blockade in cHL. Altogether, our findings reveal extensive immune rewiring and complementary roles of CD4+ T cells, B cells and IL1?+ monocytes in the response to PD-1 blockade and suggest that these features can be captured with a peripheral blood test.
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Nature Communications
Neoadjuvant Anti-PD1 Immunotherapy for Surgically Accessible Recurrent Glioblastoma: Clinical and Molecular Outcomes of a Stage 2 Single-Arm Expansion Cohort
McFaline-Figueroa JR, Youssef GC, Huang R, Lee EQ, Nayak L, Chukwueke U, Beroukhim R, Batchelor TT, Chiocca EA, Doherty L, Stefanik J, Partridge K, Spearman A, Myers A, Westergaard C, Russ A, Lavallee M, Smokovich A, LaForest-Roys C, Garcia Fox R, McCluskey C, Bi WL, Arnaout O, Peruzzi P, Cosgrove GR, Ligon KL, Arrillaga-Romany I, Reardon DA, Wen PY
Glioblastoma is immunologically "cold" and resistant to single-agent immune-checkpoint inhibitors (ICI). Our previous study of neoadjuvant pembrolizumab in surgically-accessible recurrent glioblastoma identified a molecular signature of response to ICI and suggested that neoadjuvant pembrolizumab may improve survival. To increase the power of this observation, we enrolled an additional 25 patients with a primary endpoint of evaluating the cell cycle gene signature associated with neoadjuvant pembrolizumab and performed bulk-RNA seq on resected tumor tissue (NCT02852655). Neoadjuvant pembrolizumab was associated with suppression of cell cycle/cancer proliferation genes and upregulation of T-cell/interferon-related gene expression. This signature was unique to patients treated with neoadjuvant pembrolizumab and was an independent positive risk factor for survival. Our results demonstrate a clear pharmacodynamic effect of anti-PD1 therapy in glioblastoma and identify pathways that may mediate resistance. However, we did not confirm a survival benefit to neoadjuvant pembrolizumab in recurrent glioblastoma and our secondary endpoint of PFS-6 was 19.5% (95% CI: 9.29-41.2%) for the pooled neoadjuvant cohorts. Our new data suggests some patients may exhibit innate resistance to pre-surgical ICI and require other concomitant therapies to sensitize effectively.
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Blood Advances
Elotuzumab in Combination with Pomalidomide, Bortezomib, and Dexamethasone in Relapsed and Refractory Multiple Myeloma
Yee AJ, Laubach JP, Nadeem O, O’Donnell E, Bianchi G, Branagan AR, Schlossman RL, Shapiro SJ, Harrington CC, Burke JN, Gammon MT, Lively KJ, Andrade DX, Redd RA, Lohr JG, Anderson KC, Richardson PG, Raje N
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Cancer Research
Selective Enhancer Gain-of-Function Deregulates MYC Expression in Multiple Myeloma
Rahmat M, Clement K, Alberge JB, Sklavenitis-Pistofidis R, Fulco CP, Heilpern-Mallory D, Dorfman D, Engreitz JM, Getz G, Pinello L, Ghobrial IM
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Cell Reports
ZBTB7A is a Modulator of KDM5-Driven Transcriptional Networks in Basal Breast Cancer
DiCiaccio B, Seehawer M, Li Z, Patmanidis A, Bui T, Foidart P, Nishida J, Papanastasiou M, Reiter AH, Qiu X, Li R, Jiang Y, Huang XY, Brown M, Long HW, Polyak K
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Clinical Cancer Research
Prospective Trial of Biomarker-Guided Surveillance for HPV-Positive Oropharynx Cancer Using Plasma Tumor Tissue Modified Viral HPV DNA
Rettig EM, Schoenfeld JD, Miller J, Sargent B, Carey E, Margalit DN, Sehgal K, Sethi RKV, Uppaluri R, Tishler RB, Goguen LA, Annino DJ, Jo VY, Wong KS, Guenette JP, Haddad RI, Hanna GJ
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Cold Spring Harbor Perspectives in Medicine
Epigenetic Therapies
Bourgeois W, Armstrong SA, Heikamp EB
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JCI Insight
BPDCN MYB Fusions Regulate Cell Cycle Genes, Impair Differentiation, and Induce Myeloid-Dendritic Cell Leukemia
Booth CA, Bouyssou JM, Togami K, Armand O, Rivas HG, Yan K, Rice S, Lachtara EM, Rheinbay E, DeCaprio JA, Lane AA
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OncoImmunology
Circulating Cytokine Associations with Clinical Outcomes in Melanoma Patients Treated with Combination Nivolumab Plus Ipilimumab
Chen J, Tarantino G, Severgnini M, Baginska J, Giobbie-Hurder A, Weirather JL, Manos M, Russell JD, Pfaff KL, Rodig SJ, Huang AY, Brennick R, Nazzaro M, Hathaway E, Holovatska M, Manuszak C, Ranasinghe S, Liu D, Hodi FS
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Oncologist
Incidence of Patient-Reported Fatigue Developing on Palbociclib and Endocrine Therapy for Advanced HR+ HER2- Breast Cancer
Rahman SA, Poort H, Schrag D, Tung SC, Zhou ES, Wiley A, Finkelstein LB, Elguenaoui E, Nolan M, Mayer EL, Joffe H
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