Roman Gulati
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Roman Gulati has a total of 466 co-authors. Here are the top co-authors by number of shared publications:
- Ruth Etzioni
Fred Hutch Cancer Center, Seattle, WA
53 shared publications - Peter S. Nelson
Human Biology Division, Fred Hutchinson Cancer Center, Seattle, USA
49 shared publications - Michael Thomas Schweizer
Fred Hutchinson Cancer Center 4 Clinical Research Division Seattle, Washington
47 shared publications - John K. Lee
Division of Hematology/Oncology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
46 shared publications - Eva Corey
Department of Urology, University of Washington, Seattle, WA 98195, USA
44 shared publications - Bruce Montgomery
Clinical Research Division Fred Hutchinson Cancer Center Seattle WA USA
43 shared publications
Latest Publications
- 12 October 2026
- Published by Springer Nature in Supportive Care in Cancer
- Vol. 34 (11) , 1087
Impact of a dietary and exercise intervention on health-related quality of life of prostate cancer patients on active surveillance with overweight/obesity: a secondary analysis of a randomized controlled trial
- 12 October 2026
- Published by Springer Nature in Supportive Care in Cancer
- Vol. 34 (11) , 1087
Impact of a dietary and exercise intervention on health-related quality of life of prostate cancer patients on active surveillance with overweight/obesity: a secondary analysis of a randomized controlled trial
- Open Access
Recommendations of the American Cancer Society workshop on design, conduct, analysis, and reporting of multicancer early detection trials with late‐stage incidence end points and post‐trial ongoing evaluation—The Peachtree Consensus
Background: In a new era of multicancer early detection (MCED), late‐stage incidence has been proposed as an end point for the evaluation of test efficacy, marking a departure from the established end point of cancer mortality. Methods: The American Cancer Society convened a panel of 15 academic researchers with expertise in cancer screening evaluation to develop principles assuring the validity and interpretability of MCED trials with late‐stage incidence end points and advancing implementation of tests with potential for meaningful clinical utility. Results: The definition of late‐stage cancer is critical and affects study design, interpretation, and outcomes. The authors of the Peachtree Consensus recommend considering cancer‐type–specific definitions of late stage and paying attention to comparability of staging intensity in both trial arms. The duration of screening and the follow‐up interval after the last screen should be chosen based on the preclinical early stage and late‐stage durations of the target cancer types. The authors also recommend reporting aggregate results and results for individual cancer types as numbers permit and summarizing relative and absolute benefits. Predicted mortality reductions should be calculated to contextualize a late‐stage result and the inputs and assumptions involved should be reported. Given a significant late‐stage reduction suggestive of meaningful clinical benefit, the authors support launching consortium and demonstration studies while continuing to track trial mortality outcomes. Conclusions: Trials with late‐stage incidence end points that are conducted using these principles should support the development of evidence‐based screening guidelines as well as the creation of accessible data resources for observational and modeling studies.
Open AccessRecommendations of the American Cancer Society workshop on design, conduct, analysis, and reporting of multicancer early detection trials with late‐stage incidence end points and post‐trial ongoing evaluation—The Peachtree Consensus
Background: In a new era of multicancer early detection (MCED), late‐stage incidence has been proposed as an end point for the evaluation of test efficacy, marking a departure from the established end point of cancer mortality. Methods: The American Cancer Society convened a panel of 15 academic researchers with expertise in cancer screening evaluation to develop principles assuring the validity and interpretability of MCED trials with late‐stage incidence end points and advancing implementation of tests with potential for meaningful clinical utility. Results: The definition of late‐stage cancer is critical and affects study design, interpretation, and outcomes. The authors of the Peachtree Consensus recommend considering cancer‐type–specific definitions of late stage and paying attention to comparability of staging intensity in both trial arms. The duration of screening and the follow‐up interval after the last screen should be chosen based on the preclinical early stage and late‐stage durations of the target cancer types. The authors also recommend reporting aggregate results and results for individual cancer types as numbers permit and summarizing relative and absolute benefits. Predicted mortality reductions should be calculated to contextualize a late‐stage result and the inputs and assumptions involved should be reported. Given a significant late‐stage reduction suggestive of meaningful clinical benefit, the authors support launching consortium and demonstration studies while continuing to track trial mortality outcomes. Conclusions: Trials with late‐stage incidence end points that are conducted using these principles should support the development of evidence‐based screening guidelines as well as the creation of accessible data resources for observational and modeling studies.
- 16 September 2026
- Published by American Association for Cancer Research (AACR) in Cancer Epidemiology, Biomarkers & Prevention
Expected late-stage reduction under extended screening in the first multicancer screening trial
The first multi-cancer screening trial did not meet its primary endpoint of reducing late-stage (stage III/IV) incidence after three screening rounds. We used a previously developed multi-cancer natural history model to assess whether continued annual screening could have produced larger benefit. The model was calibrated to match observed late-stage reductions by screening round and aggregate episode sensitivity for the trial’s 12 prespecified cancers, then used to project late-stage reductions over 10 annual screening rounds. Among the 15 best-calibrated models, early-stage preclinical detectable period ranged from 0.46 to 1.36 years, and per-cancer early-stage sensitivities from 30%-60% of previously reported sensitivities for clinically diagnosed cancers. The calibrated models reproduced the excess late-stage incidence in round 1 and increasing reductions thereafter. With continued screening, late-stage reductions by screening round plateaued at 13%-17% (range across 15 models), while cumulative late-stage reduction reached 9%-13% by round 10.
- 16 September 2026
- Published by American Association for Cancer Research (AACR) in Cancer Epidemiology, Biomarkers & Prevention
Expected late-stage reduction under extended screening in the first multicancer screening trial
The first multi-cancer screening trial did not meet its primary endpoint of reducing late-stage (stage III/IV) incidence after three screening rounds. We used a previously developed multi-cancer natural history model to assess whether continued annual screening could have produced larger benefit. The model was calibrated to match observed late-stage reductions by screening round and aggregate episode sensitivity for the trial’s 12 prespecified cancers, then used to project late-stage reductions over 10 annual screening rounds. Among the 15 best-calibrated models, early-stage preclinical detectable period ranged from 0.46 to 1.36 years, and per-cancer early-stage sensitivities from 30%-60% of previously reported sensitivities for clinically diagnosed cancers. The calibrated models reproduced the excess late-stage incidence in round 1 and increasing reductions thereafter. With continued screening, late-stage reductions by screening round plateaued at 13%-17% (range across 15 models), while cumulative late-stage reduction reached 9%-13% by round 10.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S7 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Distribution of KLK2 and STEAP1 single cell mRNA expression in cells from ARPC samples across MSK scRNA dataset.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S7 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Distribution of KLK2 and STEAP1 single cell mRNA expression in cells from ARPC samples across MSK scRNA dataset.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S6 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 6 shows representative KLK2 and STEAP1 IHC micrographs with the H-score threshold of 20.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S6 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 6 shows representative KLK2 and STEAP1 IHC micrographs with the H-score threshold of 20.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S3 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
IHC H-scores of UW-RA samples, including phenotypes as defined by IHC.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S3 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
IHC H-scores of UW-RA samples, including phenotypes as defined by IHC.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S4 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 4 shows representative KLK2 IHC micrographs of LuCaP PDX tumors across phenotypes.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S4 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 4 shows representative KLK2 IHC micrographs of LuCaP PDX tumors across phenotypes.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S9 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 9 contains donut plots showing the distribution of tumor expressing only one marker, both markers or neither marker for each KLK2-STEAP1, STEAP1-PSMA, and KLK2-PSMA pair across molecular phenotypes and across H-score thresholds.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S9 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Supplementary Figure 9 contains donut plots showing the distribution of tumor expressing only one marker, both markers or neither marker for each KLK2-STEAP1, STEAP1-PSMA, and KLK2-PSMA pair across molecular phenotypes and across H-score thresholds.
- Open Access
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S15 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Cell surface targets and their associated limma values from KLK2-high vs KLK2-low and STEAP1-high vs STEAP1-low differential expression analyses.
- 2 September 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S15 from Integrative Surface Antigen Profiling of KLK2 and STEAP1 in Advanced Prostate Cancer
Cell surface targets and their associated limma values from KLK2-high vs KLK2-low and STEAP1-high vs STEAP1-low differential expression analyses.