Artificial intelligence analysis of CCNG1 expression in tumors: A novel biomarker in development for anti-CCNG1 targeted therapy.

Authors

Erlinda Gordon

Erlinda Maria Gordon

Sarcoma Oncology Research Center, Santa Monica, CA

Erlinda Maria Gordon , Nadezhda Omelchenko , Caroline Eid , Samantha Jeffrey , Zhanna Shekhovtsova , Kushal Suryamohan , Elan Younesi , Amir Ahari , Neal Shiv Chawla , Don Arlen Brigham , Michael Morse , Sant P. Chawla

Organizations

Sarcoma Oncology Research Center, Santa Monica, CA, Dornsife College of Letters, University of Southern California, Los Angeles, CA, University of California Los Angeles, Los Angeles, CA, Boston Gene, Inc., Waltham, MA, BostonGene, Waltham, MA, City of Hope Comprehensive Cancer Center, Duarte, CA, Aveni Foundation, Santa Monica, CA, Duke University Medical Center, Durham, NC

Research Funding

No funding received
None.

Background: Metastatic cancer is associated with an invariably fatal outcome. Therefore, innovative therapies are urgently needed. Although expanded access for DeltaRex-G, a tumor targeted retrovector encoding a CCNG1 inhibitor gene, is on-going for an intermediate-size (n = up to 40) population of advanced sarcoma and pancreatic cancer, more data is needed to identify patients who are likely to benefit from DeltaRex-G gene therapy. In this study, we retrospectively analyzed CCNG1 expression in archived tumors of patients who were previously treated with DeltaRex-G and who are active candidates for DeltaRex-G therapy. Methods: Archived formalin-fixed paraffin embedded (FFPE) tumor specimens (n = 58) from patients with solid malignancies who are actively followed at the Cancer Center of Southern California were collected, processed, and subjected to RNA sequencing. Briefly, RNA-seq libraries were sequenced to generate 50 million reads that were aligned using Kallisto v0.42.4 to GENCODE v23 transcripts with default parameters. Only protein-coding, IGH/K/L- and TCR-related transcripts were retained for downstream processing, resulting in 20,062 protein coding genes. Gene expression was quantified as transcripts per million (TPM) and log2-transformed (Goldman et al., 2020). A gene expression level is presented as low/medium/high depending on the expression level of a such gene in patients of the reference cohort. Low = < 17%; Medium = 17%-83%; High = > 83%. Results: Thirty-two male and 26 female subjects, ages ranging from 16 to 86 years were studied. Forty-nine (84.4%) patients had sarcoma, 3 (5.2%) had urothelial carcinoma, 2 (3.5%) had breast carcinoma, 2 (3.5%) had pancreatic cancer, 1 (1.7%) had Sertoli cell tumor, 1 (1.7%) had adenocarcinoma of appendix. Eleven (19%) tumors showed high CCNG1 expression, 44 (76%) tumors had medium expression, and 3 (5.2%) tumors had low CCNG1 expression. Of note, the tumor of one 14-year survivor with metastatic pancreatic adenocarcinoma in sustained remission had 24 % CCNG1 expression, one 3-year survivor with metastatic chondrosarcoma metastatic to lung with stable disease had 74% CCNG1 expression, one 2-year survivor with early stage HR+ HER2+ breast cancer in remission had 23% CCNG1 expression, and one 2-year survivor with early stage triple negative breast cancer in remission had 74% CCNG1 expression. Conclusions: Taken together, these data indicate that (1) Medium to high CCNG1 expression was found in 95% of tumors studied, and (2) Patients with medium CCNG1 expression who received DeltaRex-G had clinical benefit and are alive in sustained remission or with stable disease 2-14 years from DeltaRex-G treatment initiation, and (3) Prospective studies are warranted to correlate CCNG1 expression level and response to DeltaRex-G therapy.

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Abstract Details

Meeting

2023 ASCO Annual Meeting

Session Type

Publication Only

Session Title

Publication Only: Developmental Therapeutics—Molecularly Targeted Agents and Tumor Biology

Track

Developmental Therapeutics—Molecularly Targeted Agents and Tumor Biology

Sub Track

New Targets and New Technologies (non-IO)

Citation

J Clin Oncol 41, 2023 (suppl 16; abstr e15083)

DOI

10.1200/JCO.2023.41.16_suppl.e15083

Abstract #

e15083

Abstract Disclosures