Look beyond the familiar cohorts

Underused research opportunities

Used far less than resources of similar size, age, breadth and access tier - so the questions they can answer are still open. How the gap is modelled.

An opportunity signal, not a quality score

Underused is not poor quality, and it is not a promise of a finding. The 378 datasets with no citable accession are never labelled either way: their reuse cannot be measured at all.

Where the 602 records stand

Reuse can only be measured for datasets with an accession that articles quote.

378 63%No citable accessionReuse cannot be traced through the literature.
92 15%Citable, but no article has quoted it yetNothing to date availability from, so the model does not apply.
112 19%Reused about as expectedWithin the range the model predicts.
20 3%Underused opportunityWell below prediction, and few articles in absolute terms.

Every dataset we could assess, 132 in all

Articles that analyzed the data against the number expected. Below the dashed line a dataset is labeled underexplored if it also has fewer than 25 articles. Hover a dot for its numbers, click to open the dataset.

Underused opportunities (20)Other assessed datasets (112)Reused as expectedLabel threshold: below 2.8× less than expected

Show all 132 datasets as a table

Dataset	Analyzed it	Expected	Index
NSCLC Radiogenomicsunderused opportunity	3	173	-5.44
Burkitt Lymphoma Genome Sequencing Projectunderused opportunity	5	60	-3.35
Neuroblastoma	49	458	-3.20
Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)underused opportunity	6	45	-2.71
CPTAC UCEC Confirmatory Studyunderused opportunity	1	12	-2.68
NCI Cancer Model Development for the Human Cancer Model Initiativeunderused opportunity	6	40	-2.54
Broad Institute - Medulloblastomaunderused opportunity	0	4	-2.44
High-Risk Wilms Tumor	72	385	-2.40
Molecular Profiling to Predict Response to Treatment for Acute Lymphoblastic Leukemiaunderused opportunity	0	4	-2.37
VA Research Precision Oncology Programunderused opportunity	0	4	-2.30
Philadelphia-Negative Neutrophilic Leukemias (CNL/aCML/MDS/MPNu)underused opportunity	1	8	-2.15
Count Me In (CMI): The Angiosarcoma (ASC) Projectunderused opportunity	0	3	-2.11
Clinical Resistance to Crenolanib in Acute Myeloid Leukemia Due to Diverse Molecular Mechanismsunderused opportunity	0	3	-2.09
Acute Myeloid Leukemia	137	570	-2.05
CPTAC GBM Discovery Studyunderused opportunity	1	7	-2.04
CPTAC non-ccRCC Studyunderused opportunity	1	7	-1.98
Clear Cell Sarcoma of the Kidneyunderused opportunity	4	18	-1.95
Exceptional Respondersunderused opportunity	2	10	-1.85
Rhabdoid Tumor	39	137	-1.78
CPTAC STAD Studyunderused opportunity	1	6	-1.75
CPTAC-Brain, Head and Neck, Kidney, Lung, Pancreas, Uterusunderused opportunity	25	85	-1.73
Molecular Profiling to Predict Response to Treatment - Wilms Tumorunderused opportunity	1	6	-1.71
Proteogenomics of Gastric Cancerunderused opportunity	4	15	-1.66
Therapeutic Targets in Breast Cancer Xenograftsunderused opportunity	1	5	-1.66
CTSP Diffuse Large B-Cell Lymphoma (DLBCL) CALGB 50303	4	13	-1.45
HIV+ Tumor Molecular Characterization Project - Diffuse Large B-Cell Lymphoma	0	2	-1.35
CPTAC CCRCC Discovery Study	4	12	-1.34
Pheochromocytoma and Paraganglioma	43	109	-1.33
Acute Lymphoblastic Leukemia - Phase II	42	105	-1.29
Testicular Germ Cell Tumors	53	127	-1.24
Comprehensive genomic characterization of radiation-related papillary thyroid cancer in the Ukraine	1	4	-1.23
CPTAC LUAD Confirmatory Study	2	5	-1.08
PTRC TNBC	3	7	-1.07
HIV+ Tumor Molecular Characterization Project - Cervical Cancer	21	44	-1.04
PTRC HGSOC Frozen Validation	0	1.0	-0.98
Beat AML Baseline Clinical	2	5	-0.94
CPTAC LSCC Discovery Study	19	36	-0.90
Uterine Carcinosarcoma	33	61	-0.88
CDDP Integrative Analysis of Lung Adenocarcinoma (Phase 2)	0	0.8	-0.87
Prospective Breast BI Phosphoproteome	2	4	-0.87
Functional Genomic Landscape of Acute Myeloid Leukemia	22	40	-0.82
Thymoma	41	68	-0.71
CPTAC LUAD Discovery Study	22	36	-0.70
CPTAC RCC Combined Study	0	0.6	-0.68
Kidney Renal Papillary Cell Carcinoma	163	250	-0.61
KNCC Glioblastoma Evolution	2	4	-0.59
CPTAC-Breast, Colon, Ovary	8	12	-0.56
APOLLO1: Proteogenomic characterization of lung adenocarcinoma	2	3	-0.54
Academia Sinica LUAD100	10	14	-0.42
CPTAC UCEC Discovery Study	13	18	-0.41
Pediatric Brain Cancer Pilot Study	6	8	-0.39
KU CCA Discovery Study	0	0.3	-0.39
KU CCA Discovery Study - Global proteome	0	0.3	-0.39
TCGA Breast Cancer Phosphoproteome	3	4	-0.37
Academia Sinica LUAD ICPC-B	1	2	-0.34
NCI-7 Cell Line Panel	0	0.2	-0.32
Acute Lymphoblastic Leukemia - Phase III	105	126	-0.26
Prospective Colon PNNL Phosphoproteome Lumos	2	2	-0.19
PTRC TNBC PDX	1	1	-0.13
Acute Lymphoblastic Leukemia - Phase I	14	15	-0.07
CPTAC PDA Discovery Study	19	20	-0.06
Mesothelioma	67	68	-0.03
Pancreas Cancer Organoid Profiling	3	3	-0.00
Count Me In (CMI): The Metastatic Breast Cancer (MBC) Project	3	3	0.00
CPTAC CCRCC Confirmatory Study	3	3	0.00
Genomic Variation in Diffuse Large B Cell Lymphomas	24	23	0.05
Osteosarcoma	244	233	0.06
PTRC Triple-negative Breast Cancer Mitotic Vulnerability Study	1	0.9	0.07
PTRC BRCA CALGB40601 Neoadjuvant Trial	1	0.9	0.08
Adrenocortical Carcinoma	113	102	0.15
KNCC Medulloblastoma - KBRI	0	0.0	0.18
Count Me In (CMI): The Metastatic Prostate Cancer (MPC) Project	2	2	0.21
Thyroid Carcinoma	363	308	0.24
Kidney Chromophobe	95	80	0.25
Prospective Ovarian PNNL Proteome Qeplus	3	2	0.26
Uterine Corpus Endometrial Carcinoma	387	320	0.28
CPTAC HNSCC Discovery Study	18	14	0.30
TCGA Colon Cancer Proteome	5	4	0.32
NCC iCC	2	1	0.35
TCGA Ovarian JHU Proteome	6	4	0.37
Yonsei Cancer Center - TNBC	0	0.0	0.48
Genomic Characterization CS-MATCH-0007 Arm Z1D	1	0.4	0.49
KU PDAC Discovery Study	1	0.3	0.60
Uveal Melanoma	113	70	0.69
Prospective Breast BI Acetylome	1	0.2	0.70
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma	83	51	0.70
TCGA Ovarian JHU Glycoproteome	1	0.2	0.72
TCGA Ovarian PNNL Proteome	7	4	0.76
Sarcoma	192	109	0.81
KNCC Medulloblastoma - KNCC	0	0.0	0.81
Molecular Characterization Initiative (MCI)	1	0.1	0.82
Prospective Ovarian JHU Intact Glycoproteome	1	0.1	0.85
Prospective Ovarian JHU N-linked Glycosite-containing peptide	1	0.1	0.85
Prospective Ovarian PNNL Phosphoproteome Lumos	1	0.1	0.86
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma	416	221	0.91
CPTAC PDAC BioTExt	1	0.0	0.95
Ovarian Serous Cystadenocarcinoma	641	326	0.97
Multiple Myeloma CoMMpass Study	125	63	0.99
Prospective Breast BI Proteome	19	9	1.00
TCGA Ovarian PNNL Phosphoproteome Velos Qexactive	1	0.0	1.03
Lung Squamous Cell Carcinoma	643	307	1.06
HBV-Related Hepatocellular Carcinoma	27	12	1.15
Sampling techniques for enrichment of PDAC	2	0.3	1.26
Head and Neck Squamous Cell Carcinoma	758	313	1.27
Oral Squamous Cell Carcinoma Study	1	0.0	1.34
Glioblastoma Multiforme	1,399	538	1.38
Genomic Characterization of Metastatic Castration Resistant Prostate Cancer	27	10	1.38
Esophageal Carcinoma	308	111	1.47
Skin Cutaneous Melanoma	724	260	1.48
Proteogenomic analysis of tumors from young women with breast cancer	0	0.0	1.48
VU Normal Colon Epithelium	1	0.0	1.49
Cholangiocarcinoma	195	68	1.51
KU PDAC Discovery Study - Global proteome	3	0.3	1.60
Ukrainian National Research Center for Radiation Medicine Trio Study	1	0.0	1.63
Brain Lower Grade Glioma	620	196	1.66
Prospective Ovarian JHU Proteome	15	4	1.69
Prostate Adenocarcinoma	997	306	1.70
Prospective Colon VU Proteome	3	0.1	1.83
Bladder Urothelial Carcinoma	894	247	1.85
TCGA Breast Cancer Proteome	18	4	1.88
Kidney Renal Clear Cell Carcinoma	1,245	315	1.98
Prospective Colon PNNL Proteome Qeplus	19	4	1.99
Genomic Characterization CS-MATCH-0007 Arm Z1I	1	0.0	2.00
Rectum Adenocarcinoma	482	112	2.10
CGU OSCC APOBEC3A	1	0.0	2.13
Stomach Adenocarcinoma	1,405	293	2.26
Breast Invasive Carcinoma	2,373	412	2.52
Colon Adenocarcinoma	1,838	298	2.62
Acute Myeloid Leukemia	441	71	2.63
Pancreatic Adenocarcinoma	854	115	2.88
Lung Adenocarcinoma	2,831	325	3.12
Liver Hepatocellular Carcinoma	2,568	275	3.22

NSCLC Radiogenomics
nsclc_radiogenomicsIDC211 cases
3 articles analyzed these data. About 173 would be expected. Its publication has been cited 202 times.
Clinical dataRadiology imagingWhole genome sequencing
Open questions these data could answer
Do quantitative CT or PET features predict gene expression programs in lung adenocarcinoma?
How reproducible are radiomic features across CT and PET acquisitions of the same tumor?
All 3 reviewed questions
Analyzed it3
Expected~173
Reuse gap index -5.44
Burkitt Lymphoma Genome Sequencing Project
CGCI-BLGSPGDC + IDC388 casesfollow-up 15.7 months
5 articles analyzed these data. About 60 would be expected.
Clinical dataCopy numberHistopathology slidesmicroRNARadiology imagingBulk RNA sequencingStructural variantsTargeted DNA panelWhole genome sequencing
Population: substantial non-white representation
Analyzed it5
Expected~60
Reuse gap index -3.35
Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)
FM-ADGDC18,004 cases
6 articles analyzed these data. About 45 would be expected.
Clinical dataTargeted DNA panel
Population: race not recorded
Open questions these data could answer
What are the mutation frequencies and co-occurrence patterns of clinically targeted genes in a large real-world oncology population?
How does the mutational landscape of clinically sequenced patients differ from that of research cohorts such as TCGA?
All 3 reviewed questions
Analyzed it6
Expected~45
Reuse gap index -2.71
CPTAC UCEC Confirmatory Study
PDC000443PDC159 casesfollow-up 3.8 years
1 article analyzed these data. About 12 would be expected.
AcetylomeGlycoproteomePhosphoproteomeProteome (mass spectrometry)
Analyzed it1
Expected~12
Reuse gap index -2.68
NCI Cancer Model Development for the Human Cancer Model Initiative
HCMI-CMDCGDC + IDC805 casesfollow-up 15.8 months
6 articles analyzed these data. About 40 would be expected.
Clinical dataCopy numberHistopathology slidesDNA methylationmicroRNARadiology imagingBulk RNA sequencingStructural variantsWhole genome sequencingWhole exome sequencing
Open questions these data could answer
How faithfully do patient-derived models retain the genomics of their source tumor?
Can a usable model system be found for a rare cancer that currently has none?
All 3 reviewed questions
Analyzed it6
Expected~40
Reuse gap index -2.54
Broad Institute - Medulloblastoma
PDC000430PDC46 cases
No article has analyzed these data. About 4 would be expected.
AcetylomePhosphoproteomeProteome (mass spectrometry)
Population: race not recorded
Analyzed it0
Expected~4
Reuse gap index -2.44
Molecular Profiling to Predict Response to Treatment for Acute Lymphoblastic Leukemia
MP2PRT-ALLGDC1,510 casesfollow-up 8.3 years
No article has analyzed these data. About 4 would be expected.
Clinical dataCopy numberBulk RNA sequencingStructural variantsWhole genome sequencingWhole exome sequencing
Population: non-US enrolment recorded
Open questions these data could answer
Which genomic features predict failure of protocolised induction therapy in paediatric B-cell acute lymphoblastic leukaemia?
Do Hispanic or Latino children carry a different genomic landscape or a different response profile, and does genomics explain any of the known outcome disparity?
All 4 reviewed questions
Analyzed it0
Expected~4
Reuse gap index -2.37
VA Research Precision Oncology Program
VAREPOP-APOLLOGDC + IDC41 casesfollow-up 13.2 months
No article has analyzed these data. About 4 would be expected.
Radiology imagingTargeted DNA panel
Analyzed it0
Expected~4
Reuse gap index -2.30
Philadelphia-Negative Neutrophilic Leukemias (CNL/aCML/MDS/MPNu)
OHSU-CNLGDC176 casesfollow-up 19.1 months
1 article analyzed these data. About 8 would be expected.
Bulk RNA sequencingStructural variantsWhole exome sequencing
Population: race not recorded
Analyzed it1
Expected~8
Reuse gap index -2.15
Count Me In (CMI): The Angiosarcoma (ASC) Project
CMI-ASCGDC36 cases
No article has analyzed these data. About 3 would be expected.
Clinical dataBulk RNA sequencingStructural variantsWhole exome sequencing
Analyzed it0
Expected~3
Reuse gap index -2.11
Clinical Resistance to Crenolanib in Acute Myeloid Leukemia Due to Diverse Molecular Mechanisms
BEATAML1.0-CRENOLANIBGDC56 cases
No article has analyzed these data. About 3 would be expected.
Whole exome sequencing
Population: race not recorded
Analyzed it0
Expected~3
Reuse gap index -2.09
CPTAC GBM Discovery Study
PDC000245PDC111 casesfollow-up 12.8 months
1 article analyzed these data. About 7 would be expected.
AcetylomeLipidomeMetabolomePhosphoproteomeProteome (mass spectrometry)
Population: substantial non-white representation
Analyzed it1
Expected~7
Reuse gap index -2.04
CPTAC non-ccRCC Study
PDC000466PDC44 casesfollow-up 4.5 years
1 article analyzed these data. About 7 would be expected.
GlycoproteomeMetabolomePhosphoproteomeProteome (mass spectrometry)
Analyzed it1
Expected~7
Reuse gap index -1.98
Clear Cell Sarcoma of the Kidney
TARGET-CCSKGDC13 casesfollow-up 6.0 years
4 articles analyzed these data. About 18 would be expected.
Clinical dataCopy numberDNA methylationBulk RNA sequencingStructural variantsWhole genome sequencing
Analyzed it4
Expected~18
Reuse gap index -1.95
Exceptional Responders
EXCEPTIONAL_RESPONDERS-ERGDC84 casesfollow-up 5.4 years
2 articles analyzed these data. About 10 would be expected.
Clinical dataBulk RNA sequencingStructural variantsTargeted DNA panelWhole exome sequencing
Analyzed it2
Expected~10
Reuse gap index -1.85
CPTAC STAD Study
PDC000622PDC193 casesfollow-up 2.0 years
1 article analyzed these data. About 6 would be expected.
AcetylomeGlycoproteomeMetabolomeOtherPhosphoproteomeProteome (mass spectrometry)Ubiquitylome
Open questions these data could answer
How do distinct post-translational modification layers diverge from each other and from transcript abundance on the same tumors?
Does protein degradation signaling, read from the ubiquitylome, identify tumor subgroups invisible to transcriptomics?
All 4 reviewed questions
Analyzed it1
Expected~6
Reuse gap index -1.75
CPTAC-Brain, Head and Neck, Kidney, Lung, Pancreas, Uterus
CPTAC-3GDC1,866 casesfollow-up 22.9 months
25 articles analyzed these data. About 85 would be expected. Its publication has been cited 688 times.
Copy numberDNA methylationmicroRNABulk RNA sequencingSingle-cell RNA sequencingStructural variantsTargeted DNA panelWhole genome sequencingWhole exome sequencing
Open questions these data could answer
Which genomic alterations have consequences at the protein level, and which do not?
What molecular features predict recurrence across cancer types?
All 3 reviewed questions
Analyzed it25
Expected~85
Reuse gap index -1.73
Molecular Profiling to Predict Response to Treatment - Wilms Tumor
MP2PRT-WTGDC52 casesfollow-up 22.5 months
1 article analyzed these data. About 6 would be expected.
Clinical dataCopy numbermicroRNABulk RNA sequencingStructural variantsWhole genome sequencing
Analyzed it1
Expected~6
Reuse gap index -1.71
Proteogenomics of Gastric Cancer
PDC000216PDC80 cases
4 articles analyzed these data. About 15 would be expected.
GlycoproteomePhosphoproteomeProteome (mass spectrometry)
Population: race not recorded
Analyzed it4
Expected~15
Reuse gap index -1.66
Therapeutic Targets in Breast Cancer Xenografts
PDC000304PDC27 cases
1 article analyzed these data. About 5 would be expected.
PhosphoproteomeProteome (mass spectrometry)
Population: race not recorded
Analyzed it1
Expected~5
Reuse gap index -1.66