Investigator

Kwong-Kwok Wong

Professor · University of Texas MD Anderson Cancer Center, Gynecology Oncology and Reproductive Medicine

KWKwong-Kwok Wong
Papers(5)
Molecular Profiling a…The prognostic value …Spatial Transcriptomi…Integrated multi-omic…Impact of estrogen re…
Collaborators(10)
Michael ChurchmanMitchell I. EdelsonOliver DorigoPeter G. RoseRobert L. ColemanRobert L. HollisSaketh R. GuntupalliSammy Ferri-BorgognoSamuel C. MokStephanie Gaillard
Institutions(8)
The University Of Tex…University of Edinbur…Thomas Jefferson Univ…Stanford Cancer Insti…Cleveland ClinicThe US Oncology Netwo…University Of Colorad…Johns Hopkins School …

Papers

Molecular Profiling and Tumor Biomarker Analysis of GOG281/LOGS: A Positive Late-Phase Trial of Trametinib for Recurrent/Persistent Low-Grade Serous Ovarian Carcinoma

Abstract Purpose: Low-grade serous ovarian carcinoma (LGSOC) is a distinct form of ovarian cancer characterized by younger patient age and relative chemoresistance. The GOG281/LOGS trial (NCT02101788) investigated the efficacy of the MEK inhibitor trametinib compared with physician’s choice standard-of-care (SOC) in patients with LGSOC with persistent/recurrent disease. The study demonstrated significantly improved progression-free survival (PFS) in the trametinib-treated arm. Experimental Design: Two hundred and sixty patients with recurrent/persistent LGSOC were enrolled and randomly assigned in GOG281. We performed molecular analysis of 170 patients with available tumor specimens, comprising whole-exome sequencing and phospho-ERK (pERK) IHC, to identify biomarkers of clinical benefit from trametinib. The demographics of the translational cohort (n = 170) were comparable with those of the total trial cohort. Results: High tumor pERK expression (greater than the median histoscore of 140) was associated with significantly prolonged PFS with trametinib treatment versus SOC (median 20.1 vs. 5.6 months, log-rank P < 0.0001; test for interaction P = 0.023). Tumors harboring canonical RAS–RAF–MAPK mutations (KRAS/BRAF/NRAS: 44/134, 32.8% of cases) had a higher response rate to trametinib (50.0% vs. 8.3%; Barnard’s P = 0.0004; test for interaction P = 0.054), but KRAS/BRAF/NRAS status was not predictive of prolonged PFS (test for interaction P = 0.719). KRAS amplification (n = 5 without KRAS/NRAS/BRAF mutation) and mutation of MAPK-associated genes (n = 25 without KRAS/NRAS/BRAF mutation or KRAS copy number gain) expanded the number of cases with identifiable MAPK defects to 55.2%, but consideration of these events did not improve the discrimination of trametinib responders. Chr1p loss (49% of cases) was associated with lower pERK expression (P = 0.021). Conclusions: This exploratory analysis suggests that pERK expression and mutation of KRAS/BRAF/NRAS are candidate biomarkers of improved PFS and response to trametinib, respectively.

The prognostic value of MEK pathway–associated estrogen receptor signaling activity for female cancers

Abstract Background Other than for breast cancer, endocrine therapy has not been highly effective for gynecologic cancers. Endocrine therapy resistance in estrogen receptor positive gynecologic cancers is still poorly understood. In this retrospective study, we examined the estrogen receptor (ER) signaling pathway activities of breast, ovarian, endometrial, and cervical cancers to identify those that may predict endocrine therapy responsiveness. Methods Clinical and genomic data of women with breast and gynecological cancers were downloaded from cBioPortal for Cancer Genomics. Estrogen receptor alpha (ESR1) expression level and sample-level pathway enrichment scores (EERES) were calculated to classify patients into four groups (low/high ESR1 and low/high EERES). Correlation between ESR1/EERES score and survival was further validated with RNAseq data from low-grade serous ovarian cancer. Pathway analyses were performed among different ESR1/EERES groups to identify genes that correlate with endocrine resistance, which are validated using Cancer Cell Line Encyclopedia gene expression and Genomics of Drug Sensitivity in Cancer data. Results We identified a novel combined prognostic value of ESR1 expression and the corresponding estrogen response signaling (EERES score) for breast cancer. The combined prognostic value (ESR1/EERES) may be applicable to other gynecologic cancers. More importantly, we discovered that ER signaling can cross-regulate MEK pathway activation. We identified downstream genes in the MEK pathway (EPHA2, INAVA, MALL, MPZL2, PCDH1, and TNFRSF21) that are potential endocrine therapy response biomarkers. Conclusion This study demonstrated that targeting both the ER and the ER signaling activity related MEK pathway may aid the development of endocrine therapy strategies for personalized medicine.

Spatial Transcriptomics Depict Ligand–Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors

Abstract Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand–receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted. Significance: Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383

Integrated multi-omic analysis of low-grade ovarian serous carcinoma collected from short and long-term survivors

Abstract Background Low-grade serous ovarian cancer (LGSOC) is a rare disease that occurs more frequently in younger women than those with high-grade disease. The current treatment is suboptimal and a better understanding of the molecular pathogenesis of this disease is required. In this study, we compared the proteogenomic analyses of LGSOCs from short- and long-term survivors (defined as < 40 and > 60 months, respectively). Our goal was to identify novel mutations, proteins, and mRNA transcripts that are dysregulated in LGSOC, particularly in short-term survivors. Methods Initially, targeted sequencing of 409 cancer-related genes was performed on 22 LGSOC and 6 serous borderline ovarian tumor samples. Subsequently, whole-genome sequencing analysis was performed on 14 LGSOC samples (7 long-term survivors and 7 short-term survivors) with matched normal tissue samples. RNA sequencing (RNA-seq), quantitative proteomics, and phosphoproteomic analyses were also performed. Results We identified single-nucleotide variants (SNVs) (range: 5688–14,833 per sample), insertion and deletion variants (indels) (range: 880–1065), and regions with copy number variants (CNVs) (range: 62–335) among the 14 LGSOC samples. Among all SNVs and indels, 2637 mutation sites were found in the exonic regions. The allele frequencies of the detected variants were low (median12%). The identified recurrent nonsynonymous missense mutations included KRAS, NRAS, EIF1AX, UBR5, and DNM3 mutations. Mutations in DNM3 and UBR5 have not previously been reported in LGSOC. For the two samples, somatic DNM3 nonsynonymous missense mutations in the exonic region were validated using Sanger sequencing. The third sample contained two missense mutations in the intronic region of DNM3, leading to a frameshift mutation detected in RNA transcripts in the RNA-seq data. Among the 14 LGSOC samples, 7754 proteins and 9733 phosphosites were detected by global proteomic analysis. Some of these proteins and signaling pathways, such as BST1, TBXAS1, MPEG1, HBA1, and phosphorylated ASAP1, are potential therapeutic targets. Conclusions This is the first study to use whole-genome sequencing to detect somatic mutations in LGSOCs with matched normal tissues. We detected and validated novel mutations in DNM3, which were present in 3 of the 14 samples analyzed. Additionally, we identified novel indels, regions with CNVs, dysregulated mRNA, dysregulated proteins, and phosphosites that are more prevalent in short-term survivors. This integrated proteogenomic analysis can guide research into the pathogenesis and treatment of LGSOC.

Impact of estrogen receptor expression on prognosis of ovarian cancer according to antibody clone used for immunohistochemistry: a meta-analysis

Abstract Background The prognostic value of the expression of estrogen receptor (ER) subtypes ER⍺ and ERβ in ovarian cancer has previously been evaluated by meta-analyses. However, the results are contradictory and controversial. Methods We conducted an updated meta-analysis with stringent inclusion criteria to ensure homogeneous studies to determine the effect of ER subtypes on ovarian cancer prognosis. Articles were retrieved by systematic search of PubMed and Web of Science for articles dated up to June 2021. Only studies with known hazard ratio (HR) and antibody clone for immunochemistry (IHC) were included. Pooled HRs with the corresponding 95% confidence intervals (CIs) were calculated for the effect of ER⍺ and ERβ expression on ovarian cancer patient progression-free survival (PFS) and overall survival (OS). Results A total of 17 studies were included, of which 11 and 13 studies examined the relationships between ER⍺ expression and PFS and OS, respectively, and 5 and 7 studies examined the relationships between ERβ expression and PFS and OS, respectively. Neither ER⍺ expression (random-effects model; HR = 0.99, 95% CI = 0.83–1.18) nor ERβ expression (fixed-effects model; HR = 0.94, 95% CI = 0.69–1.27) was associated with PFS. Random-effects models showed that ER⍺ expression (HR = 0.81, 95% CI = 0.64–1.02) and ERβ expression (HR = 0.75, 95% CI = 0.50–1.13) were only marginally and not significantly associated with better OS. Subgroup analysis revealed that ER⍺ expression determined using antibody clone 1D5 (HR = 0.75, 95% CI = 0.64–0.88) and ERβ expression determined using ERβ1-specific-antibody clone PPG5/10 or EMR02 (HR = 0.65, 95% CI = 0.50–0.86) were associated with significantly better OS, but ER expression determined using other antibodies was not. Conclusions In conclusion, a higher ER⍺ expression and ERβ expression are significantly associated with a better survival of ovarian cancer patients, but the results from previous prognostic studies are significantly dependent on the choice of specific ER antibody clones used in immunohistochemistry analysis.

139Works
5Papers
32Collaborators
Ovarian NeoplasmsBiomarkers, TumorCell Line, TumorNeoplasm GradingPrognosisBreast NeoplasmsDrug Resistance, Neoplasm

Positions

2011–

Professor

University of Texas MD Anderson Cancer Center · Gynecology Oncology and Reproductive Medicine

2005–

Associate Professor

The University of Texas MD Anderson Cancer Center · Gyencologic Oncology and Reproductive Medicine

2001–

Assistant Professor

Baylor College of Medicine · Pediatrics

1995–

Senior Research Scientist

Pacific Northwest National Laboratory · Molecular Biosciences

1990–

Research Scientist

California Institute of Biological Research

Education

1990

Ph.D.

The Chinese University of Hong Kong · Biology

1987

M.Phil.

The Chinese University of Hong Kong · Biology

Keywords
Ovarian Cancermolecular biologycancer biologygenomicsbacterial geneticsUterine leiomyoma and leiomyosarcoma