Victoria Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Victoria Wang has a total of 186 co-authors. Here are the top co-authors by number of shared publications:
- David R. Patton
Center for Biomedical Informatics and Information Technology, NCI, NIH, Bethesda, MD
74 shared publications - Lyndsay N. Harris
Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
74 shared publications - Peter J. O'Dwyer
University of Pennsylvania, Philadelphia, PA
74 shared publications - Stanley R. Hamilton
City of Hope National Medical Center, Duarte, CA
74 shared publications - Robert James Gray
Dana Farber Cancer Institute – ECOG-ACRIN Biostatistics Center, Boston, Massachusetts
69 shared publications - Alice P. Chen
Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, USA
43 shared publications
Latest Publications
- Open Access
- 2 November 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Data1 from ERK1/2 Inhibitor Ulixertinib in Pancancer Patients with BRAF Fusions or Non-V600E/K Mutations: Results from the NCI-MATCH ECOG-ACRIN Trial (EAY131) Subprotocol Z1L
Supplementary Tables including study representativeness table.
- 2 November 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Data1 from ERK1/2 Inhibitor Ulixertinib in Pancancer Patients with BRAF Fusions or Non-V600E/K Mutations: Results from the NCI-MATCH ECOG-ACRIN Trial (EAY131) Subprotocol Z1L
Supplementary Tables including study representativeness table.
- 11 August 2026
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 32 (21) , 4782-4790
ERK1/2 Inhibitor Ulixertinib in Pancancer Patients with BRAF Fusions or Non-V600E/K Mutations: Results from the NCI-MATCH ECOG-ACRIN Trial (EAY131) Subprotocol Z1L
Purpose: Mutations in BRAF at codons other than V600 (non-V600) and BRAF fusions confer dependence on the RAF–MEK–ERK pathway. Subprotocol Z1L (EAY131-Z1L) investigated the clinical activity of ulixertinib (ERK1/2 inhibitor) in patients with tumors harboring these alterations. Patients and Methods: In this single-arm study, patients with BRAF non-V600 mutations or BRAF fusions were given ulixertinib orally, at a dose of 600 mg twice daily, continuously for each 28-day cycle until progression or intolerability. The primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), 6-month PFS, and overall survival (OS). Results: Of the 34 eligible patients, the median age was 66.5; 50% were women; and 88%, 9%, and 3% were White, Black, and Asian, respectively. Seventy-four percent of patients had an Eastern Cooperative Oncology Group performance status of 1. The median number of prior therapies was 4. Tumor types included multiple gastrointestinal malignancies (n = 16), lung cancer (n = 3), and melanoma (n = 3). No patients achieved complete response or partial response, resulting in an ORR of 0%. Stable disease was the best response in 7 of 26 centrally confirmed cases. The median PFS was 1.7 months [90% confidence interval (CI), 1.1–2.2]; the 6-month PFS rate was 5% (90% CI, 0.6%–17.7%); and the median OS was 3.5 months (90% CI, 1.9–5.4). Twenty (57%) patients had grade 3 toxicity, and 1 (3%) patient had grade 4 toxicity as their worst toxicity; there was no grade 5 toxicity. Conclusions: Ulixertinib had no demonstrable evidence of clinical activity in this small, heavily pretreated population of patients with tumors harboring BRAF fusions or non-V600E/non-V600K BRAF mutations.
- 11 August 2026
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 32 (21) , 4782-4790
ERK1/2 Inhibitor Ulixertinib in Pancancer Patients with BRAF Fusions or Non-V600E/K Mutations: Results from the NCI-MATCH ECOG-ACRIN Trial (EAY131) Subprotocol Z1L
Purpose: Mutations in BRAF at codons other than V600 (non-V600) and BRAF fusions confer dependence on the RAF–MEK–ERK pathway. Subprotocol Z1L (EAY131-Z1L) investigated the clinical activity of ulixertinib (ERK1/2 inhibitor) in patients with tumors harboring these alterations. Patients and Methods: In this single-arm study, patients with BRAF non-V600 mutations or BRAF fusions were given ulixertinib orally, at a dose of 600 mg twice daily, continuously for each 28-day cycle until progression or intolerability. The primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), 6-month PFS, and overall survival (OS). Results: Of the 34 eligible patients, the median age was 66.5; 50% were women; and 88%, 9%, and 3% were White, Black, and Asian, respectively. Seventy-four percent of patients had an Eastern Cooperative Oncology Group performance status of 1. The median number of prior therapies was 4. Tumor types included multiple gastrointestinal malignancies (n = 16), lung cancer (n = 3), and melanoma (n = 3). No patients achieved complete response or partial response, resulting in an ORR of 0%. Stable disease was the best response in 7 of 26 centrally confirmed cases. The median PFS was 1.7 months [90% confidence interval (CI), 1.1–2.2]; the 6-month PFS rate was 5% (90% CI, 0.6%–17.7%); and the median OS was 3.5 months (90% CI, 1.9–5.4). Twenty (57%) patients had grade 3 toxicity, and 1 (3%) patient had grade 4 toxicity as their worst toxicity; there was no grade 5 toxicity. Conclusions: Ulixertinib had no demonstrable evidence of clinical activity in this small, heavily pretreated population of patients with tumors harboring BRAF fusions or non-V600E/non-V600K BRAF mutations.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Figure 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
External validation of MI Clarity M3T for late DR in the TAILORx translational cohort. A, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in all patients. B, Multivariable Cox analysis assessing the independent prognostic value of the MI Clarity M3T risk label, adjusted for clinical covariates. C, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in arms B and C (ITT). LDR, late distant recurrence.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
External validation of MI Clarity M3T for late DR in the TAILORx translational cohort. A, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in all patients. B, Multivariable Cox analysis assessing the independent prognostic value of the MI Clarity M3T risk label, adjusted for clinical covariates. C, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in arms B and C (ITT). LDR, late distant recurrence.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Figure 4 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Effect of ELT on DR across subgroups in the NSABP B-42 translational cohort.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 4 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Effect of ELT on DR across subgroups in the NSABP B-42 translational cohort.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Clinicopathologic, and treatment characteristics of patients classified as MI Clarity–High versus Low risk in the NSABP B-42 translational cohort.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Clinicopathologic, and treatment characteristics of patients classified as MI Clarity–High versus Low risk in the NSABP B-42 translational cohort.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure 4 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
External validation of MI Clarity M3T for overall distant recurrence (DR) in the TAILORx translational cohort.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure 4 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
External validation of MI Clarity M3T for overall distant recurrence (DR) in the TAILORx translational cohort.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table 11 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Clinicopathologic, and treatment characteristics of patients classified as MI Clarity–High versus Low risk in the TAILORx translational late-DR cohort.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table 11 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Clinicopathologic, and treatment characteristics of patients classified as MI Clarity–High versus Low risk in the TAILORx translational late-DR cohort.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Figure 5 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Kaplan–Meier analysis of DR comparing patients treated with ELT vs. placebo in the NSABP B-42 translational cohort. A, All patients. B, Low-risk group. C, High-risk group. High- and low-risk groups are defined by the M3T model.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 5 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Kaplan–Meier analysis of DR comparing patients treated with ELT vs. placebo in the NSABP B-42 translational cohort. A, All patients. B, Low-risk group. C, High-risk group. High- and low-risk groups are defined by the M3T model.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Figure 2 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Kaplan–Meier analysis of DR in the NSABP B-42 translational cohort according to MI Clarity M3T risk groups. A, All patients. B, ELT-treated group. C, Placebo group. High- and low-risk groups are defined by the M3T model.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 2 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Kaplan–Meier analysis of DR in the NSABP B-42 translational cohort according to MI Clarity M3T risk groups. A, All patients. B, ELT-treated group. C, Placebo group. High- and low-risk groups are defined by the M3T model.
- Open Access
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table 10 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Time-dependent predictive performance of the M3T model for ELT benefit in clinical subgroups with violated proportional hazards (PH) assumption.
- 31 July 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table 10 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Time-dependent predictive performance of the M3T model for ELT benefit in clinical subgroups with violated proportional hazards (PH) assumption.