Evidence map›Paper›PMID 40025512›Full record

ArticleMolecular cancer2025

BCL-2 dependence is a favorable predictive marker of response to therapy for chronic lymphocytic leukemia.

Stephen Jun Fei Chong, Junyan Lu, Rebecca Valentin, Timothy Z Lehmberg, Jie Qing Eu, Jing Wang, Fen Zhu, Li Ren Kong, Stacey M Fernandes, Jeremy Zhang and 7 more

Abstract read
In one paragraph

Article in Molecular cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. BCL-2 and BCL-xL in Cancer: Regulation, Function, and Therapeutic Targeting.International journal of molecular sciences · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Stephen Jun Fei Chong *Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Junyan Lu *Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
Rebecca Valentin *Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Timothy Z LehmbergDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Jie Qing EuCancer Science Institute of Singapore, N2CR, NUS, Singapore, Singapore.
Jing WangDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Fen ZhuDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Li Ren KongCancer Science Institute of Singapore, N2CR, NUS, Singapore, Singapore.
Stacey M FernandesDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Jeremy ZhangDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Charles HerbauxDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Boon Cher GohCancer Science Institute of Singapore, N2CR, NUS, Singapore, Singapore.
Jennifer R BrownDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
Carsten U NiemannDepartment of Hematology, Rigshospitalet, Copenhagen, Denmark.
Wolfgang Huber *European Molecular Biology Laboratory, Heidelberg, Germany.
Thorsten Zenz *Department of Medical Oncology and Hematology, University of Zurich & University Hospital Zurich, Zurich, Switzerland.
Matthew S Davids *Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA. matthew_davids@dfci.harvard.edu.

Funding

Physician Scientist Training in Cancer ResearchT32CA009172 · NCI · DANA-FARBER CANCER INSTITUTE · PI Jennifer R Brown, James A. DeCaprio · 1985 to 2026
$17.1M
ProteomicsP01CA206978 · NCI · DANA-FARBER CANCER INST · PI WU, CATHERINE JU-YING · 2016 to 2025
$17.0M
NCI NIH HHS P01 CA206978NCI NIH HHS T32 CA009172
6 · The paper itself

Abstract

backgroundEstablished genetic biomarkers in chronic lymphocytic leukemia (CLL) have been useful in predicting response to chemoimmunotherapy but are less predictive of response to targeted therapies. With several such targeted therapies now approved for CLL, identifying novel, non-genetic predictive biomarkers of response may help to select the optimal therapy for individual patients.

methodsWe coupled data from a functional precision medicine technique called BH3-profiling, which assesses cellular cytochrome c loss levels as indicators for survival dependence on anti-apoptotic proteins, with multi-omics data consisting of targeted and whole-exome sequencing, genome-wide DNA methylation profiles, RNA-sequencing, protein and functional analyses, to identify biomarkers for treatment response in CLL patients.

resultsWe initially studied 73 CLL patients from a discovery cohort. We found that greater dependence on the anti-apoptotic BCL-2 protein was associated with prognostically favorable genetic biomarkers. Furthermore, BCL-2 dependence was strongly associated with gene expression patterns and signaling pathways that suggest a more targeted drug-sensitive milieu and was predictive of drug responses. We subsequently demonstrated that these associations were causal in cell lines and additional CLL patient samples. To validate the findings from our discovery cohort and in vitro studies, we utilized primary CLL cells from 54 additional patients treated on a prospective, phase-2 clinical trial of the BTK inhibitor ibrutinib given in combination with chemoimmunotherapy (fludarabine, cyclophosphamide, rituximab) and confirmed in this independent dataset that higher BCL-2 dependence predicted favorable clinical response, independent of the genetic background of the CLL cells.

conclusionWe comprehensively defined BCL-2 dependence as a potential functional and predictive biomarker of treatment response in CLL, underscoring the importance of characterizing apoptotic signaling in CLL to stratify patients beyond genetic markers and identifying novel combinations to exploit BCL-2 dependence therapeutically. Our approach has the potential to help optimize targeted therapy combinations for CLL patients.

Indexed as

Biomarkers, TumorLeukemia, Lymphocytic, Chronic, B-CellProto-Oncogene Proteins c-bcl-2AdenineAgedAntineoplastic Combined Chemotherapy ProtocolsApoptosisCell Line, TumorDNA MethylationFemaleHumansMaleMiddle AgedPiperidinesPrognosisPyrazolesAdenineBCL2 protein, humanBiomarkers, TumoribrutinibPiperidinesProto-Oncogene Proteins c-bcl-2PyrazolesPyrimidines

Identifiers

PMID40025512
PMCPMC11874845

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.