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. 2023 Nov 14;29(22):4660-4668.
doi: 10.1158/1078-0432.CCR-23-1078.

Assessing the Genomic Landscape of Cervical Cancers: Clinical Opportunities and Therapeutic Targets

Claire F Friedman  1   2 , Vignesh Ravichandran  3 , Kathryn Miller  4 , Chad Vanderbilt  5 , Qin Zhou  6 , Alexia Iasonos  6 , Malavika Vivek  3 , Pamela Mishra  3 , Mario M Leitao Jr  4   7 , Vance Broach  4   7 , Yukio Sonoda  4   7 , Chrisann Kyi  1   2 , Dmitriy Zamarin  1   2 , Roisin E O'Cearbhaill  1   2 , Jason Konner  1   2 , Michael F Berger  3   5 , Britta Weigelt  5 , Amir Momeni Boroujeni  5 , Kay J Park  5 , Carol Aghajanian  1   2 , David B Solit  1   2   3   8 , Mark T A Donoghue  3
Affiliations

Assessing the Genomic Landscape of Cervical Cancers: Clinical Opportunities and Therapeutic Targets

Claire F Friedman et al. Clin Cancer Res. .

Abstract

Purpose: Tumor genomic profiling is increasingly used to guide treatment strategy in patients with cancer. We integrated tumor genomic, clinical demographic, and treatment response data to assess how prospective tumor-normal sequencing impacted treatment selection in patients with cervical cancer.

Experimental design: Cervical cancers were prospectively analyzed using the MSK-IMPACT (Memorial Sloan Kettering Cancer Center - Integrated Mutation Profiling of Actionable Cancer Targets) next-generation sequencing panel. Clinical data, including histology, stage at diagnosis, treatment history, clinical trial enrollment and outcomes, date of last follow-up, and survival status were obtained from medical records.

Results: A total of 177 patients with cervical cancer (squamous, 69; endocervical adenocarcinoma, 50; gastric type, 22; adenosquamous, 21; and other, 15) underwent MSK-IMPACT testing. The most prevalent genomic alterations were somatic mutations or amplifications in PIK3CA (25%), ERBB2 (12%), KMT2C (10%), and KMT2D (9%). Furthermore, 13% of patients had high tumor mutational burden (TMB >10 mut/Mb), 3 of which were also microsatellite instability-high (MSI-H). Thirty-seven percent of cases had at least one potentially actionable alteration designated as a level 3B mutational event according to the FDA-recognized OncoKB tumor mutation database and treatment classification system. A total of 30 patients (17%) were enrolled on a therapeutic clinical trial, including 18 (10%) who were matched with a study based on their MSK-IMPACT results. Twenty patients (11%) participated in an immune checkpoint inhibition study for metastatic disease; 2 remain progression free at >5 years follow-up.

Conclusions: Tumor genomic profiling can facilitate the selection of targeted/immunotherapies, as well as clinical trial enrollment, for patients with cervical cancer.

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Figures

Figure 1. Patient pathologic characteristics. A, Comparison of TCGA and MSK-IMPACT cervical cancer cohorts based on histology, HPV positivity, and stage at diagnosis. B, Tumor site samples on which MSK-IMPACT sequencing was performed. C, OS analysis of the MSK-IMPACT survival cohort.
Figure 1.
Patient pathologic characteristics. A, Comparison of TCGA and MSK-IMPACT cervical cancer cohorts based on histology, HPV positivity, and stage at diagnosis. B, Tumor site samples on which MSK-IMPACT sequencing was performed. C, OS analysis of the MSK-IMPACT survival cohort.
Figure 2. Genomic alterations by histologic subtype. A, Oncoprint depicting genomic alterations by histology type. Detailed annotations of “other” histologies available in Supplementary Fig. S3. B, Distribution of oncogenic drivers by histology type. C, Tumor mutational burden by histology type. MSI-H, microsatellite instability–high.
Figure 2.
Genomic alterations by histologic subtype. A, Oncoprint depicting genomic alterations by histology type. Detailed annotations of “other” histologies available in Supplementary Fig. S3. B, Distribution of oncogenic drivers by histology type. C, Tumor mutational burden by histology type. MSI-H, microsatellite instability–high.
Figure 3. Clinical outcomes of MSK-IMPACT testing. A, Distribution of genomic alterations with OncoKB levels of evidence. B, Swimmers plot of clinical outcomes on clinical trials by target. C, Near complete response to combined immune checkpoint inhibition after 6 months of therapy in a patient with adenosquamous carcinoma. The patient went on to achieve a complete response and is disease free. D, Major response to combined immune checkpoint inhibition in a patient with small cell neuroendocrine carcinoma after 6 months of therapy. The patient went on to achieve a complete response and is disease free. CR, complete response; CT CAP, computerized tomography scan of the chest, abdomen, and pelvis; MSI-H, microsatellite instability–high; NE, not evaluable; PD, progression of disease; PR, partial response; SD, stable disease; TMB-H, tumor mutational burden–high.
Figure 3.
Clinical outcomes of MSK-IMPACT testing. A, Distribution of genomic alterations with OncoKB levels of evidence. B, Swimmers plot of clinical outcomes on clinical trials by target. C, Near complete response to combined immune checkpoint inhibition after 6 months of therapy in a patient with adenosquamous carcinoma. The patient went on to achieve a complete response and is disease free. D, Major response to combined immune checkpoint inhibition in a patient with small cell neuroendocrine carcinoma after 6 months of therapy. The patient went on to achieve a complete response and is disease free. CR, complete response; CT CAP, computerized tomography scan of the chest, abdomen, and pelvis; MSI-H, microsatellite instability–high; NE, not evaluable; PD, progression of disease; PR, partial response; SD, stable disease; TMB-H, tumor mutational burden–high.
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