Dan Avi Landau
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Dan Avi Landau has a total of 787 co-authors. Here are the top co-authors by number of shared publications:
- Franco Izzo
Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
64 shared publications - Robert M. Myers
New York Genome Center, New York, USA
30 shared publications - Ross L. Levine
Memorial Sloan Kettering Cancer Center, New York, USA
30 shared publications - Aaron D. Viny
Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
28 shared publications - Abbas Nazir
Genentech, Inc., 1 DNA Way, South San Francisco, CA, USA
27 shared publications - Abdul Karzai
Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
27 shared publications
Latest Publications
- 8 September 2026
- Published by Springer Nature in Nature Reviews Cancer
- Vol. 26 (11) , 917-939
A single-cell lens into the co-evolution of genotypes and phenotypes in cancer
- 8 September 2026
- Published by Springer Nature in Nature Reviews Cancer
- Vol. 26 (11) , 917-939
A single-cell lens into the co-evolution of genotypes and phenotypes in cancer
- Open Access
- 1 July 2026
- Published by Springer Nature in Leukemia
- Vol. 40 (9) , 1-6
Single-cell DNA methylation analysis uncovers epigenetic pathways in the transformation of MDS to AML
- 1 July 2026
- Published by Springer Nature in Leukemia
- Vol. 40 (9) , 1-6
Open AccessSingle-cell DNA methylation analysis uncovers epigenetic pathways in the transformation of MDS to AML
- Open Access
- 22 June 2026
- Published by Springer Nature in Nature Genetics
- Vol. 58 (7) , 1651-1660
Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation
The progression of isocitrate dehydrogenase-mutant glioma (IDH-G) from slow-growing tumor to fatal disease is associated with transcriptional and DNA methylation changes that remain poorly understood. Here, we profiled a longitudinal cohort of 36 IDH-G samples from 19 patients by joint-capture multi-omic single-nucleus DNA methylation, single-nucleus RNA sequencing and bulk exome sequencing. We show that IDH-G progression is associated with an increase in malignant stem-like states, decreased differentiation and methylation loss, which marks tumors with worse clinical outcome. Methylation loss was uniformly observed across malignant cells within individual tumors, suggesting that it may underlie rather than result from the increase in stem-like states. Analysis of cell-state heritability and plasticity using high-resolution phylogenetic trees links DNA methylation loss to alterations in glioma cell-state encoding and heritability. Our study offers insights into how DNA methylation loss reshapes cellular transitions and how it may mark clinically more aggressive tumors across IDH-G subsets.
- 22 June 2026
- Published by Springer Nature in Nature Genetics
- Vol. 58 (7) , 1651-1660
Open AccessLongitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation
The progression of isocitrate dehydrogenase-mutant glioma (IDH-G) from slow-growing tumor to fatal disease is associated with transcriptional and DNA methylation changes that remain poorly understood. Here, we profiled a longitudinal cohort of 36 IDH-G samples from 19 patients by joint-capture multi-omic single-nucleus DNA methylation, single-nucleus RNA sequencing and bulk exome sequencing. We show that IDH-G progression is associated with an increase in malignant stem-like states, decreased differentiation and methylation loss, which marks tumors with worse clinical outcome. Methylation loss was uniformly observed across malignant cells within individual tumors, suggesting that it may underlie rather than result from the increase in stem-like states. Analysis of cell-state heritability and plasticity using high-resolution phylogenetic trees links DNA methylation loss to alterations in glioma cell-state encoding and heritability. Our study offers insights into how DNA methylation loss reshapes cellular transitions and how it may mark clinically more aggressive tumors across IDH-G subsets.
- Open Access
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Figure S15 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Estimating zygosity for variants in driver genes
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S15 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Estimating zygosity for variants in driver genes
- Open Access
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Figure S12 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Differentiation and cell cycling scores of mutant clones
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S12 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Differentiation and cell cycling scores of mutant clones
- Open Access
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Figure S10 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Differentiation score and cell cycling score validation
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S10 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Differentiation score and cell cycling score validation
- Open Access
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Figure S7 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
DNA genotyping efficiency metrics
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S7 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
DNA genotyping efficiency metrics
- Open Access
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Tables S1-S14 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Supplementary Table 1. DNA panel genomic locations (hg19). Supplementary Table 2. Metadata of samples used in study. Supplementary Table 3. RNA panel targets (hg19). Supplementary table 4. Cell line mixing variants detected (hg19). Supplementary table 5. Comparison with scDNAseq assay. Supplementary table 6. Cell line mixing RNA metadata. Supplementary Table 7. Number of reads with valid cell barcodes aligned to NOTCH1 region with mapping quality (MAPQ) greater than or less than 30. Supplementary table 8. Donor samples variants detected (hg19). Supplementary table 9. Cell type annotations. Supplementary table 10. RNA module scoring. Supplementary table 11. Donor samples clone call and RNA metadata. Supplementary table 12. ESO6 variants detected (hg19). Supplementary table 13. ESO6 NOTCH1 germline variants detected (hg19). Supplementary table 14.ESO6 clone call and RNA metadata.
- 1 April 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Tables S1-S14 from Genotype-to-Phenotype Mapping of Somatic Clonal Mosaicism via Single-Cell Co-Capture of DNA Mutations and mRNA Transcripts
Supplementary Table 1. DNA panel genomic locations (hg19). Supplementary Table 2. Metadata of samples used in study. Supplementary Table 3. RNA panel targets (hg19). Supplementary table 4. Cell line mixing variants detected (hg19). Supplementary table 5. Comparison with scDNAseq assay. Supplementary table 6. Cell line mixing RNA metadata. Supplementary Table 7. Number of reads with valid cell barcodes aligned to NOTCH1 region with mapping quality (MAPQ) greater than or less than 30. Supplementary table 8. Donor samples variants detected (hg19). Supplementary table 9. Cell type annotations. Supplementary table 10. RNA module scoring. Supplementary table 11. Donor samples clone call and RNA metadata. Supplementary table 12. ESO6 variants detected (hg19). Supplementary table 13. ESO6 NOTCH1 germline variants detected (hg19). Supplementary table 14.ESO6 clone call and RNA metadata.