Surfaceome profiling to reveal unique therapeutic vulnerabilities in transcriptional subtypes of small cell lung cancer (SCLC).

Abstract

8515 Background: Effective treatment options for SCLC remain limited and new treatment approaches are needed to improve outcome. We sought to validate the initial observation in cell lines and limited tissue samples of SCLC of a differential expression of cancer/testis (CT) antigens and TACSD2 gene that encodes surface protein, Trop2 across various subtypes of SCLC. We also tested whether overall surfaceome profile as previously described in other tumor types will show hierarchical priority of expression between transcriptionally defined SCLC subtypes. Methods: We conducted a comprehensive surfaceome profiling of SCLC samples using data generated by RNA sequencing (whole transcriptome) at Caris Life Sciences (Phoenix, AZ). SCLC tumors were stratified into 5 subgroups (SCLC-A/N/Y/P and -mixed) based on the relative expression of the four transcription factors. Expression values were converted to z-scores (the expression value for each gene is normalized to the average expression of that specific gene such that the z-score reflects the number of standard deviations above or below the average). The highest positive z-score among the 4 transcription factors determined subgroup. If all transcription factor z-scores for a given sample were negative, the sample was assigned to ‘Mixed’ subgroup. Significance was tested by Chi-square, Fisher’s exact test, or Mann-Whitney U test. Results: We employed data generated from 674 SCLC samples; median age of 66 years and male (48.7%). The SCLC subtype distribution was 241 (35.8%), 120 (17.8%), 40 (5.9%), 143 (21.2%), 130 (19.3%) for types A, N, P, Y and mixed respectively. Supervised analysis for TACSTD2 expression showed highest levels in YAP1 subtype and was overall significantly increased in SCLC-Y (̃3-fold) and SCLC-P (̃2-fold) subtypes compared to A, N and mixed subtypes. Similarly, SCLC-Y subtype showed the highest median expression as well as the strongest correlation with most TACSTD2-interacting and regulatory genes. A top 10 list of candidate surface protein gene out of 3699 surfaceome genes was defined for each subtype based on the strength of correlation. The top candidate surface protein gene and CT antigen gene respectively by subtype were: SCN3A (r = 0.7033, p = 1.08E-101) and NOL4, (r = 0.574, p = 2.46E-60) for SCLC-A; SSTR2, (r = 0.742, p = 8.18E-119) and TMEFF1, (r = 0.3601, p = 4.53E-22) for SCLC-N; TMPRSS13 (r = 0.5699, p = 2.64E-59) and LY6K (r = 0.4778, p = 9.80E-40) for SCLC-P; and CYBRD1 (r = 0.8559, p = 1.18E-194) and CTAGE5 (r = 0.5521, p = 4.95E-55) for SCLC-Y. Conclusions: SCLC-Y subtype showed the highest expression of TACSTD2 and its interacting and regulatory genes. This subtype could serve as an enrichment factor for antibody-drug-construct targeting TROP2. Several candidate CT antigens and surfaceome genes showing strong correlation with lineage-defining transcription factors offer additional therapeutic targets in SCLC.