Welcome to Dana-Farber's Research News
January 15, 2026
This twice-monthly newsletter highlights recently published research where Dana-Farber faculty are listed as first or senior authors. The information is pulled from PubMed and this issue notes papers published from December 16 - 31.
If you are a Dana-Farber faculty member and you think your paper is missing from Research News, please let us know by emailing dfciresearchnews@dfci.harvard.edu.
|
Blood Acalabrutinib to Assail CLL in the Frail Ryan CE, Davids MS BACKGROUND: The average hazard is a summary measure of event time distributions with a given time window, [0, ?], and allows intuitive interpretation as an average person-time incidence rate over the time window. This metric is calculated as the ratio of the cumulative incidence probability at ? to the restricted mean survival time at ? and can be estimated through non-parametric methods and thus robust. While previously proposed for randomized trials, its use in comparative effectiveness research remains underexplored. METHODS: We evaluate inference procedures for the difference and ratio of average hazards from two comparative groups, using six common confounding adjustment methods for survival functions, including direct standardization, inverse probability of treatment weighting (IPTW), propensity score matching, empirical likelihood, and augmented IPTW (AIPTW). Extensive simulation studies under varying model specification are conducted to assess bias, variance, coverage probability, and width of confidence interval. We apply the method to data from the preference cohort in the CANVAS study. RESULTS: All adjustment methods achieved satisfactory performance; AIPTW was notably robust under partial model misspecification. CONCLUSIONS: Using difference in average hazards and ratio of average hazards as estimands, when combined with common confounding adjustment methods, is feasible and reliable for comparative effectiveness research. The average hazard-based analysis provides a practical alternative to the traditional hazard ratio approach for quantifying the magnitude of the intervention effect on survival outcomes. |
|
|
|
Blood CEBPA Repression by MECOM Blocks Differentiation to Drive Aggressive Leukemias Fleming TJ, Antoszewski M, Lambo S, Gundry MC, Piussi R, Wahlster L, Shah S, Reed FE, Dong KD, Paulo JA, Gygi SP, Mimoso C, Goldman SR, Adelman K, Perry JA, Pikman Y, Stegmaier K, Barrachina MN, Machlus KR, Hovestadt V, Voit RA, Sankaran VG Acute myeloid leukemias (AMLs) have an overall poor prognosis with many high-risk cases co-opting stem cell gene regulatory programs, but the mechanisms through which these programs are propogated remain poorly understood. The increased expression of the stem cell transcription factor, MECOM, underlies a key driver mechanism in largely incurable AMLs. However, how MECOM results in such aggressive AML phenotypes remains unknown. To address existing experimental limitations, we engineered and applied targeted protein degradation with functional genomic readouts to demonstrate that MECOM promotes malignant stem cell-like states by directly repressing prodifferentiation gene regulatory programs. Remarkably and unexpectedly, a single node in this network, a MECOM-bound cis-regulatory element located 42 kilobase (kb) downstream of the myeloid differentiation regulator CEBPA is both necessary and sufficient for maintaining MECOM-driven leukemias. Importantly, the targeted activation of this regulatory element promotes differentiation of these aggressive AMLs and reduces leukemia burden in vivo. These findings suggest a broadly applicable approach for functionally dissecting oncogenic gene regulatory networks to inform improved therapeutic strategies. |
|
|
|
Cell Inhibition of Oligomeric BAX by an Anti-Apoptotic Dimer Newman CE, Gygi MA, DeAngelo TM, Camara CM, Mintseris J, Yu E, Harvey EP, Hauseman ZJ, Godes M, Gehtman J, Cathcart AM, Gygi SP, Bird GH, Walensky LD BAX is a pro-apoptotic BCL-2 protein that resides in the cytosol as a monomer until triggered by cellular stress to form an oligomer that permeabilizes mitochondria and induces apoptosis. The paradigm for apoptotic blockade involves heterodimeric interactions between pro- and anti-apoptotic monomers. Here, we find that full-length BCL-w forms a distinctive, symmetric dimer (BCL-wD) that dissociates oligomeric BAX (BAXO), inhibits mitochondrial translocation, promotes retrotranslocation, blocks membrane-porating activity, and influences apoptosis induction of cells. Structure-function analyses revealed discrete conformational changes upon BCL-w dimerization and reciprocal structural impacts upon BCL-wD and BAXO interaction. Small-angle X-ray scattering (SAXS) analysis demonstrated that BAXO disrupts membranes by inducing negative Gaussian curvature, which is reversed by positive Gaussian curvature exerted by BCL-wD. Systematic truncation and mutagenesis dissected the core features of BCL-wD activity-dimerization, BAXO engagement, and membrane interaction. Our studies reveal a downstream layer of apoptotic control mediated by protein and membrane interactions of higher-order BCL-2 family multimers. |
|
|
|
Journal of Clinical Oncology Ye Z, Mojahed-Yazdi R, Zapaishchykova A, Tak D, Mahootiha M, Pardo JCC, Zielke J, Zha Y, Guthier C, Tishler RB, Margalit DN, Schoenfeld JD, Haddad RI, Uppaluri R, Aerts HJWL, Hoebers F, Kann BH PURPOSE: Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be diagnosed via surgical pathology. We applied an automated artificial intelligence (AI) imaging platform integrating lymph node autosegmentation with ENE prediction to determine the prognostic value of the number of predicted ENE nodes. MATERIALS AND METHODS: We conducted a multisite, retrospective study of 1,733 OPC patients with pretreatment computed tomography who underwent definitive radiation therapy across three institutions. Malignant lymph nodes were segmented using a validated deep learning auto-segmentation model, and segmented lymph nodes were sequentially processed with a validated ENE prediction model to calculate number of nodes with AI-predicted ENE (AI-ENE) per patient. We evaluated associations of AI-ENE with disease outcomes using site-stratified, multivariable Cox regression, adjusting for human papillomavirus (HPV) status, smoking pack-years, tumor and nodal stage, age, and sex. We evaluated risk-stratification improvement when incorporating AI-ENE into the Radiation Therapy Oncology Group (RTOG)-0129 risk groupings and derived American Joint Committee on Cancer (AJCC) 8th edition staging with Uno C-indices and decision curve analyses. RESULTS: Overall, median AI-ENE node number was 1 (range, 0-6). AI-ENE node number was independently associated with poorer distant control (DC; hazard ratio [HR], 1.44 [95% CI, 1.23 to 1.69]; P < .001) and overall survival (OS; HR, 1.30 [95% CI, 1.16 to 1.46]; P < .001). Increasing AI-ENE node number was incrementally associated with worse outcome, particularly DC (P < .001). C-indices improved in the external data set when incorporating AI-ENE into RTOG-0129 groupings (OS: 0.70 v 0.65; DC: 0.65 v 0.57) and AJCC-8 stage (OS: 0.75 v 0.70; DC: 0.72 v 0.67; P < .001 for each). The largest improvements were observed among HPV-negative patients (C-index: +15% for OS, +14% for DC). CONCLUSION: Automated, AI-ENE node number is a novel risk factor for OPC that may better inform pretreatment risk stratification and decision-making. |
|
|
|
Journal of Clinical Oncology Drutchas A It was the kind of day that almost seemed made up – a clear, cerulean sky with sunlight bouncing off the gold dome of the State House. The contrast between this view and the drab hospital walls as I walked into my patient's room was jarring. My patient, whom I will call Suresh, sat in a recliner by the window. His lymphoma had relapsed, and palliative care was consulted to help with symptom management. The first thing I remember is that despite the havoc cancer had wreaked – sunken temples and a hospital gown slipping off his chest – Suresh had a warm, peaceful quality about him. |
|
|
|
JAMA A Scalable Model to Improve Cancer Care for Older Adults — Lo-Fi, High Impact Wright AA, Enzinger AC Individuals aged 70 years and older account for more than 40% of patients with cancer in the US and 25% of those with incident cancers. Yet older adults have been largely neglected by cancer research and care improvement efforts. Less than 3% of older patients with cancer are enrolled in oncology clinical trials, and the landscape of treatments received by older adults is not well understood. Comorbid conditions, frailty, and resource limitations can necessitate modifications to cancer treatments and render older patients more vulnerable to complications. Moreover, many may be ineligible for or choose to forgo anticancer therapy and therefore see oncology practitioners infrequently – despite experiencing symptoms and conditions that require ongoing management. These complexities beg for scalable strategies to monitor and support older adults with cancer. |
|
|
|
JAMA Oncology Sharon E IMPORTANCE: Immune-related adverse events (IRAEs) limit the use of cancer immunotherapy. Understanding the risk of severe IRAEs may help improve the use of cancer immunotherapy. OBJECTIVE: To review and assess hyperglycemic events across thousands of patients to characterize immune checkpoint inhibitor (ICI)-induced diabetes (ICI-D) using a large-scale trial conglomerate. DESIGN, SETTING, AND PARTICIPANTS: Adverse event (AE) reports related to diabetes, hyperglycemia, and acidosis were retrieved from the National Cancer Institute (NCI) Cancer Therapy Evaluation Program (CTEP) database. Trial data from June 2015 to December 2022 were analyzed. Clinical information was manually retrieved. Overall counts of patients on each trial were retrieved from central NCI data. NCI CTEP trials are hosted in both academic and community medical centers. This analysis includes patients across 158 trials who were treated with varying regimens that included programmed cell death 1 protein (PD-1) or programmed cell death 1 ligand 1 (PD-L1) inhibitors through an NCI CTEP trial for their cancer from June 2015 to December 2022. Data clarifications were requested and then data were analyzed from January 2023 to June 1, 2025. MAIN OUTCOMES AND MEASURES: Clinical characteristics differentiating ICI-D from other causes of hyperglycemia were enumerated. Cumulative incidence rates of ICI-D were calculated using trial-level data. Logistic regression was used to calculate the odds of developing ICI-D. RESULTS: In 13?966 patients across 158 trials, the overall cumulative incidence of ICI-D was low (0.52 per 100 treated patients), but incidence varied by treatment type and was lower if patients were exposed to concurrent chemotherapy (0.65% without chemotherapy vs 0.26% with chemotherapy; odds ratio [OR], 0.38; 95% CI, 0.21-0.71; P?=?.002) and higher if patients were exposed to combined immunotherapy (0.94% with combination immunotherapy vs 0.37% with PD-1/PD-L1 inhibitor monotherapy; OR, 2.68; 95% CI, 1.69-4.24). Despite these low rates, the health care burden of ICI-D was high, with 90% requiring hospitalization at diagnosis and 43% requiring intensive care. The degree of hyperglycemia can be used to differentiate different etiologies of hyperglycemia, with higher glucose levels being more likely to be due to ICI-D. CONCLUSIONS AND RELEVANCE: Results of this study suggest that ICI-D is a rare but morbid condition that varies based on the combination of ICIs with other agents. |
|
|
|
Nature Communications Shanmugam V, Tokcan N, Sullivan S, Borji M, Martin H, Newton G, Nadaf N, Hanbury S, Barrera I, Cable D, Weir J, Pinkus G, Rodig S, Uhler C, Macosko E, Shipp M, Louissaint A Jr, Chen F, Golub TR A central challenge in cancer research is to identify the secreted factors that sustain tumor cell survival. This is best exemplified in Hodgkin lymphoma, where malignant cells constitute a minor fraction of the tumor and rely on signals from the microenvironment for survival. Using genome-wide transcriptional profiling with spatial and single-cell resolution, we show that the neighborhood around malignant cells forms a distinct niche of 31 non-malignant cell types, enriched in helper T cells and myeloid cells, but depleted of plasma cells. Moreover, our spatial analysis nominates IL13 as a candidate survival factor. Recombinant IL13 augments malignant cell growth in vitro, and genome-wide loss-of-function screens across >1000 human cancer cell lines identify IL4R and IL13RA1, heterodimeric components of the IL13 receptor, as uniquely essential in Hodgkin lymphoma. Importantly, blocking antibodies phenocopy genetic inactivation. Our findings provide a biological rationale for testing IL13-directed therapies, which are already FDA-approved, in Hodgkin lymphoma. |
|
|
|
BMC Medical Research Methodology Xiong H, Connors J, Uno H |
|
|
|
Breast Cancer Research and Treatment De Placido P, Troll E, Niman SM, Ryan S, Powell M, Hazra A, Wagle N, McGillicuddy M, Lin NU, Tolaney SM, Nakhlis F, Regan MM, Lynce F |
|
|
|
Cancer Cell Targeting STING to Generate Therapeutic Anti-Tumor Immunity Fahey CG, Cordova AF, Gedeon PC, Barbie DA |
|
|
|
Cancer Epidemiology, Biomarkers, and Prevention Germline Predisposition to Oncogenic Alkylating Damage in Colorectal Cancer Cazaubiel J, Reardon B, Hofree M, Ugai T, Meyerhardt JA, Nowak JA, Giovannucci E, Ogino S, Giannakis M |
|
|
|
Cell Death and Differentiation Davern M, Turner CJ, Griffin D, Bencsics L, Chan BC, Kung JY, Olson ML, Walker Williams C, Soni S, Krotee L, Yorsz M, Antonellis G, Lizotte PH, Paweletz CP, Ryan J, Birocchi F, Almazan AJ, Sarosiek KA, Barbie D, Bhola P, Maus MV, Letai A |
|
|
|
Cell Reports Medicine A Triple-Action PROTAC for Wild-Type p53 Cancer Therapy Bird GH, Adhikary U, Schmidt MJ, Godes M, Tesar B, Camara CM, Paulo JA, Vidlak JF, DeAngelo TM, Marquez M, Gokhale P, Li R, Ho Sui SJ, Gygi SP, Walensky LD |
|
|
|
Cellular and Molecular Gastroenterology and Hepatology Novel Acinar Metaplastic States Uncovered in Exocrine Pancreas Disease Aney KJ, Jeong WJ, Koak P, Ohman AW, Nguyen CH, Wolpin BM, Nowak JA, Nissim S |
|
|
|
Contemporary Clinical Trials Onyeaka HK, Mate-Kole MN, Acheampong IA, Song MT, Amonoo HL |
|
|
|
European Urology Oncology Ravi P, Kwak L, Xie W, D'Amico A, Nguyen PL |
|
|
|
JMIR Cancer Azizoddin DR, Hassett M, Kessler D, Wright A, Gorra M, Kematick B, Chua I, Brandoff D, Lally K, Nabati L, MacIsaac S, Tulsky JA, Enzinger A |
|
|
|
Journal for ImmunoTherapy of Cancer Porter R, Bockorny B, Bullock AJ, Matulonis UA |
|
|
|
Journal of Virology Anang S, Zhang S, Ennis A, Nguyen HT, Sodroski JG |
|
|
|
Leukemia and Lymphoma Lane AA |
|
|
|
NPJ Breast Cancer Brastianos PK, Sammons S, Lin NU |
|
|
|
Scientific Reports Kim J, Ceballos-Arroyo A, Lin CH, Liu P, Jiang H, Yadav S, Wan Q, Qin L, Young GS |
|
|
|
Supportive Care in Cancer Patient and Staff Experiences with an EHR-Integrated Symptom Management Program (eSyM) in Oncology Cronin CM, Barrett F, Dias S, Paudel R, Revette A, Hassett MJ |
|
|
|
Therapy Advances in Hematology Ibrutinib Oral Suspension Bioavailability and Compatibility for Optimal Enteral Administration Route Sarosiek S |