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. 2022 Aug 30:12:844250.
doi: 10.3389/fonc.2022.844250. eCollection 2022.

DNA damage response and repair genes in advanced bone and soft tissue sarcomas: An 8-gene signature as a candidate predictive biomarker of response to trabectedin and olaparib combination

Alessandra Merlini  1   2 , Maria Laura Centomo  1   2 , Giulio Ferrero  3   4 , Giulia Chiabotto  5 , Umberto Miglio  1 , Enrico Berrino  1   5 , Giorgia Giordano  1   2 , Silvia Brusco  1   2 , Alberto Pisacane  1 , Elena Maldi  1 , Ivana Sarotto  1 , Federica Capozzi  1 , Cristina Lano  1   2 , Claudio Isella  1   2 , Giovanni Crisafulli  1   2 , Massimo Aglietta  1   2 , Angelo Paolo Dei Tos  6   7 , Marta Sbaraglia  6 , Dario Sangiolo  1   2 , Lorenzo D'Ambrosio  1   2   8 , Alberto Bardelli  1   2 , Ymera Pignochino  1   3 , Giovanni Grignani  1
Affiliations

DNA damage response and repair genes in advanced bone and soft tissue sarcomas: An 8-gene signature as a candidate predictive biomarker of response to trabectedin and olaparib combination

Alessandra Merlini et al. Front Oncol. .

Abstract

Background: Advanced and unresectable bone and soft tissue sarcomas (BSTS) still represent an unmet medical need. We demonstrated that the alkylating agent trabectedin and the PARP1-inhibitor olaparib display antitumor activity in BSTS preclinical models. Moreover, in a phase Ib clinical trial (NCT02398058), feasibility, tolerability and encouraging results have been observed and the treatment combination is currently under study in a phase II trial (NCT03838744).

Methods: Differential expression of genes involved in DNA Damage Response and Repair was evaluated by Nanostring® technology, extracting RNA from pre-treatment tumor samples of 16 responder (≥6-month progression free survival) and 16 non-responder patients. Data validation was performed by quantitative real-time PCR, RNA in situ hybridization, and immunohistochemistry. The correlation between the identified candidate genes and both progression-free survival and overall survival was investigated in the publicly available dataset "Sarcoma (TCGA, The Cancer Genome Atlas)".

Results: Differential RNA expression analysis revealed an 8-gene signature (CDKN2A, PIK3R1, SLFN11, ATM, APEX2, BLM, XRCC2, MAD2L2) defining patients with better outcome upon trabectedin+olaparib treatment. In responder vs. non-responder patients, a significant differential expression of these genes was further confirmed by RNA in situ hybridization and by qRT-PCR and immunohistochemistry in selected experiments. Correlation between survival outcomes and genetic alterations in the identified genes was shown in the TCGA sarcoma dataset.

Conclusions: This work identified an 8-gene expression signature to improve prediction of response to trabectedin+olaparib combination in BSTS. The predictive role of these potential biomarkers warrants further investigation.

Keywords: DNA damage response and repair genes; bone and soft tissue sarcomas; olaparib; predictive biomarkers; trabectedin.

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Conflict of interest statement

GGr has received fees for consulting/advisory roles from PharmaMar, Lilly, Novartis, Bayer, and Eisai. LDA received travel grant from PharmaMar and Lilly. MA has received fees for consulting/advisory roles from Bristol- Myers Squibb, Merck, and Roche. AB served in a consulting/advisory role for Illumina and Inivata. AB is cofounder and shareholder of NeoPhore. AB is a member of the NeoPhore scientific advisory board. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(A) Volcano plot showing differential expression of DDRR genes in responder vs. non-responder patients. (B) Boxplot showing differential expression of DDRR genes in responder vs. non-responder patients, with normalized expression. P-value by Wilcoxon Rank-Sum test. *p<0.05; **p<0.01.
Figure 2
Figure 2
RNA ISH of selected genes in responder vs. non-responder patients. (A) Higher expressed genes in responder patients (B) higher expressed genes in non-responders (C). Heatmap showing differential RNA ISH staining between responder and non-responder patients. ISH score was assigned by an expert pathologist on the basis of staining intensity and percentage of positive cells. P-value was calculated by Chi-square test. *p<0.05; **p<0.01.
Figure 3
Figure 3
Validation Assays (A) Expression levels of representative genes (CDKN2A, left; and APEX2, right) among responder and non-responder patients. Statistically significant differential expression was shown between the two groups (Wilcoxon rank-sum test). (B) Representative IHC staining of CDKN2A/p16 in tumor samples from responder and non-responder patients. (C) Box plot distribution of CDKN2a/p16 expression level (percentage of IHC positive cells) in responders and non-responders patients.
Figure 4
Figure 4
Oncoprint and heatmap of candidate biomarkers in the TCGA sarcoma cohort.
Figure 5
Figure 5
Kaplan-Meier curves showing Overall Sirvival in the sarcoma TCGA cohort, according to selected candidate biomarker genes (BLM, A, and MAD2L2, B).
See this image and copyright information in PMC

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