Funding in, findings out
For any dataset: the NCI award that paid to create it, and the awards that got a published finding out of it afterwards.
HTA8
Transition to Metastatic State: Lung Cancer, Pancreatic Cancer and Brain Metastasis
16 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6 of 16)
- Investigating L1CAM-dependent stem cell regeneration in metastasis · Karuna Ganesh · FY2018–2022
- Exploring the combinatorial efficacy between chemotherapy and T cell checkpoint… · MATTHEW J BOTT · FY2020–2024
- Assessment of Tim-4+ Pleural Macrophages as Negative Regulators of Anti-Tumor T… · Andrew Chow · FY2020–2022
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2019–2026
- Homologous recombination deficiency and beyond in pancreatic cancer: evaluating… · Eileen Mary O'Reilly, Alice Chia- chi Wei · FY2022–2026
- Determinants of acquired resistance in small cell lung cancer · Charles M. Rudin · FY2016–2020
The best connected are shown.
2. The dataset
3. Articles that used it
4. Awards those articles were funded by
Why one side of this chain is empty
- This dataset has no accession that articles quote, so no reuse can be traced and nothing it enabled is visible.
Awards that paid to create this data
Credited on the dataset's own marker paper.
| Award | Project | Evidence |
|---|---|---|
| K08CA230213 K08FY2018–2022$1.3M | Investigating L1CAM-dependent stem cell regeneration in metastasis | API |
| K08CA245206 K08FY2020–2024$1.3M | Exploring the combinatorial efficacy between chemotherapy and T cell checkpoint inhibition and the role of cellular senescence | API |
| K08CA248723 K08FY2020–2022$602k | Assessment of Tim-4+ Pleural Macrophages as Negative Regulators of Anti-Tumor T Cell Immunity in Lung Cancer | API |
| P30CA008748 P30FY2019–2026$224.6M | Antitumor Assessment | API |
| P50CA257881 P50FY2022–2026$23.8M | Homologous recombination deficiency and beyond in pancreatic cancer: evaluating the regulators of response to pembrolizumab and olaparib (POLAR) from the immune and genomic perspectives | API |
| R01CA197936 R01FY2016–2020$3.1M | Determinants of acquired resistance in small cell lung cancer | API |
| R01CA217169 R01FY2018–2022$2.5M | BRMS1-mediated suppression of metastases in p53 mutant lung adenocarcinoma | API |
| R01CA240472 R01FY2019–2023$2.4M | Targeting SNP BRMS1v2 A273V/A273V to reduce metastases in lung adenocarcinoma | API |
| R35CA220508 R35FY2018–2024$6.9M | Interrogating the Evolutionary Dynamics of Cancer for Clinical Benefit and Actionability | API |
| R35CA263816 R35FY2021–2026$6.3M | Novel therapeutic development for small cell lung cancer | API |
| R37CA266185 R37FY2022–2026$2.6M | Mechanisms of Dynamic Transcriptional Reprogramming in Metastasis Stem Cells | API |
| T32CA009149 T32FY1985–2022$6.1M | Immunology Research Training | API |
| U24CA213274 U24FY2017–2026$12.9M | Coordinating center for the NCI small cell lung cancer research consortium | API |
| U2CCA233284 U2CFY2018–2022$25.7M | Data Processing, Analysis and Modeling Unit | FILE |
| U54CA209975 U54FY2016–2021$23.2M | Systems biology of the tumor immune microenvironment | API |
| U54CA274492 U54FY2022–2026$27.1M | Project I: Systems analysis of tumor-stroma interactions in brain metastasis | API |
Where the links come from
NIH RePORTER maps a PMID to the awards that reported it. On a dataset’s marker paper that gives the awards which created it; on an article that used the data, the awards which funded that use.
The two sides are not measured alike. Generation is complete: a dataset has one marker paper and every award on it is resolved. Reuse is a sample - the articles using a dataset can number in the thousands, and only the few dozen strongest are retrieved and looked up. Every award shown here funded reuse; there are more that are not shown.
How far it reaches
283 datasets have an award credited with creating them, 80 with using them, and 31 both.
What is missing
No citable accession means no traceable articles; no authoritative marker paper means no attributable generation award. Absence is a gap in the record. Methods.
Start from an award instead, and see every dataset it touches.