RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer

The DNA Damage Response (DDR) landscape of endometrial cancer defines discrete disease subtypes and reveals therapeutic opportunities

Genome maintenance is an enabling characteristic that allows neoplastic cells to tolerate the inherent stresses of tumorigenesis and evade therapy-induced genotoxicity. Neoplastic cells also deploy many mis-expressed germ cell proteins termed Cancer Testes Antigens (CTAs) to promote genome maintenance and survival. Here, we present the first comprehensive characterization of the DNA Damage Response (DDR) and CTA transcriptional landscapes of endometrial cancer in relation to conventional histological and molecular subtypes. We show endometrial serous carcinoma (ESC), an aggressive endometrial cancer subtype, is defined by gene expression signatures comprising members of the Replication Fork Protection Complex (RFPC) and Fanconi Anemia (FA) pathway and CTAs with mitotic functions. DDR and CTA-based profiling also defines a subset of highly aggressive endometrioid endometrial carcinomas (EEC) with poor clinical outcomes that share similar profiles to ESC yet have distinct characteristics based on conventional histological and genomic features. Using an unbiased CRISPR-based genetic screen and a candidate gene approach, we confirm that DDR and CTA genes that constitute the ESC and related EEC gene signatures are required for proliferation and therapy-resistance of cultured endometrial cancer cells. Our study validates the use of DDR and CTA-based tumor classifiers and reveals new vulnerabilities of aggressive endometrial cancer where none currently exist.

Diffsig: Associating Risk Factors with Mutational Signatures

BACKGROUND

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

METHODS

Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations.

RESULTS

We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts.

CONCLUSIONS

Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures.

IMPACT

We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

The DNA Damage Response (DDR) landscape of endometrial cancer defines discrete disease subtypes and reveals therapeutic opportunities

Genome maintenance is an enabling characteristic that allows neoplastic cells to tolerate the inherent stresses of tumorigenesis and evade therapy-induced genotoxicity. Neoplastic cells also deploy mis-expressed germ cell proteins termed Cancer Testes Antigens (CTAs) to promote genome maintenance and survival. Here, we present the first comprehensive characterization of the DNA Damage Response (DDR) and CTA transcriptional landscapes of endometrial cancer in relation to conventional histological and molecular subtypes. We show endometrial serous carcinoma (ESC), an aggressive endometrial cancer subtype, is defined by gene expression signatures comprising members of the Replication Fork Protection Complex (RFPC) and Fanconi Anemia (FA) pathway and CTAs with mitotic functions. DDR and CTA- based profiling also defines a subset of highly aggressive endometrioid endometrial carcinomas (EEC) with poor clinical outcomes that share similar profiles to ESC yet have distinct characteristics based on conventional histological and genomic features. Using an unbiased CRISPR-based genetic screen and a candidate gene approach, we confirm that DDR and CTA genes that constitute the ESC and related EEC gene signatures are required for proliferation and therapy-resistance of cultured endometrial cancer cells. Our study validates the use of DDR and CTA-based tumor classifiers and reveals new vulnerabilities of aggressive endometrial cancer where none currently exist.

The TRIM69-MST2 signaling axis regulates centrosome dynamics and chromosome segregation

Stringent control of centrosome duplication and separation is important for preventing chromosome instability. Structural and numerical alterations in centrosomes are hallmarks of neoplastic cells and contribute to tumorigenesis. We show that a Centrosome Amplification 20 (CA20) gene signature is associated with high expression of the Tripartite Motif (TRIM) family member E3 ubiquitin ligase, TRIM69. TRIM69-ablation in cancer cells leads to centrosome scattering and chromosome segregation defects. We identify Serine/threonine-protein kinase 3 (MST2) as a new direct binding partner of TRIM69. TRIM69 redistributes MST2 to the perinuclear cytoskeleton, promotes its association with Polo-like kinase 1 (PLK1) and stimulates MST2 phosphorylation at S15 (a known PLK1 phosphorylation site that is critical for centrosome disjunction). TRIM69 also promotes microtubule bundling and centrosome segregation that requires PRC1 and DYNEIN. Taken together, we identify TRIM69 as a new proximal regulator of distinct signaling pathways that regulate centrosome dynamics and promote bipolar mitosis.

Molecular features of androgen-receptor low, estrogen receptor-negative breast cancers in the Carolina breast cancer study

PURPOSE

Androgen receptor (AR) expression is absent in 40-90% of estrogen receptor (ER)-negative breast cancers. The prognostic value of AR in ER-negative patients and therapeutic targets for patients absent in AR remains poorly explored.

METHODS

We used an RNA-based multigene classifier to identify AR-low and AR-high ER-negative participants in the Carolina Breast Cancer Study (CBCS; N = 669) and The Cancer Genome Atlas (TCGA; N = 237). We compared AR-defined subgroups by demographics, tumor characteristics, and established molecular signatures [PAM50 risk of recurrence (ROR), homologous recombination deficiency (HRD), and immune response].

RESULTS

AR-low tumors were more prevalent among younger (RFD =  + 10%, 95% CI = 4% to 16%) participants in CBCS and were associated with HER2 negativity (RFD = - 35%, 95% CI = - 44% to - 26%), higher grade (RFD =  + 17%, 95% CI = 8% to 26%), and higher risk of recurrence scores (RFD =  + 22%, 95% CI = 16.1% to 28%), with similar results in TCGA. The AR-low subgroup was strongly associated with HRD in CBCS (RFD =  + 33.3%, 95% CI = 23.8% to 43.2%) and TCGA (RFD =  + 41.5%, 95% CI = 34.0% to 48.6%). In CBCS, AR-low tumors had high adaptive immune marker expression.

CONCLUSION

Multigene, RNA-based low AR expression is associated with aggressive disease characteristics as well as DNA repair defects and immune phenotypes, suggesting plausible precision therapies for AR-low, ER-negative patients.

Molecular Features of Androgen-Receptor Low, Estrogen Receptor-Negative Breast Cancers in the Carolina Breast Cancer Study

PURPOSE

Androgen receptor (AR) expression is absent in 40-90% of estrogen receptor (ER)-negative breast cancers. The prognostic value of AR in ER-negative patients and therapeutic targets for patients absent in AR remains poorly explored.

METHODS

We used an RNA-based multigene classifier to identify AR-low and AR-high ER-negative participants in the Carolina Breast Cancer Study (CBCS; n=669) and The Cancer Genome Atlas (TCGA; n=237). We compared AR-defined subgroups by demographics, tumor characteristics, and established molecular signatures [PAM50 risk of recurrence (ROR), homologous recombination deficiency (HRD), and immune response].

RESULTS

AR-low tumors were more prevalent among Black (relative frequency difference (RFD) = +7%, 95% CI = 1% to 14%) and younger (RFD = +10%, 95% CI = 4% to 16%) participants in CBCS and were associated with HER2-negativity (RFD = -35%, 95% CI = -44% to -26%), higher grade (RFD = +17%, 95% CI = 8% to 26%), and higher risk of recurrence scores (RFD = +22%, 95% CI = 16.1% to 28%), with similar results in TCGA. The AR-low subgroup was strongly associated with HRD in CBCS (RFD = +33.3%, 95% CI = 23.8% to 43.2%) and TCGA (RFD = +41.5%, 95% CI = 34.0% to 48.6%). In CBCS, AR-low tumors had high adaptive immune marker expression.

CONCLUSION

Multigene, RNA-based low AR expression is associated with aggressive disease characteristics as well as DNA repair defects and immune phenotypes, suggesting plausible precision therapies for AR-low, ER-negative patients.

Prognostic and Predictive Value of LIV1 Expression in Early Breast Cancer and by Molecular Subtype

Background: LIV1 is a transmembrane protein that may become a new therapeutic target through the development of antibody–drug conjugates (ADCs). Few studies are available regarding the assessment of LIV1 expression in clinical breast cancer (BC) samples. Methods: We analyzed LIV1 mRNA expression in 8982 primary BC. We searched for correlations between LIV1 expression and clinicopathological data, including disease-free survival (DFS), overall survival (OS), pathological complete response to chemotherapy (pCR), and potential vulnerability and actionability to anti-cancer drugs used or under development in BC. Analyses were performed in the whole population and each molecular subtype separately. Results: LIV1 expression was associated with good-prognosis features and with longer DFS and OS in multivariate analysis. However, patients with high LIV1 expression displayed a lower pCR rate than patients with low expression after anthracycline-based neoadjuvant chemotherapy, including in multivariate analysis adjusted on grade and molecular subtypes. LIV1-high tumors were associated with higher probabilities of sensitivity to hormone therapy and CDK4/6 inhibitors and lower probabilities of sensitivity to immune-checkpoint inhibitors and PARP inhibitors. These observations were different according to the molecular subtypes when analyzed separately. Conclusions: These results may provide novel insights into the clinical development and use of LIV1-targeted ADCs by identifying prognostic and predictive value of LIV1 expression in each molecular subtype and associated vulnerability to other systemic therapies.

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

Incorporating RNA-based Risk Scores for Genomic Instability to Predict Breast Cancer Recurrence and Immunogenicity in a Diverse Population

Markers of genomic instability, including TP53 status and homologous recombination deficiency (HRD), are candidate biomarkers of immunogenicity and immune-mediated survival, but little is known about the distribution of these markers in large, population-based cohorts of racially diverse patients with breast cancer. In prior clinical trials, DNA-based approaches have been emphasized, but recent data suggest that RNA-based assessment can capture pathway differences conveniently and may be streamlined with other RNA-based genomic risk scores. Thus, we used RNA expression to study genomic instability (HRD and TP53 pathways) in context of the breast cancer immune microenvironment in three datasets (total n = 4,892), including 1,942 samples from the Carolina Breast Cancer Study, a population-based study that oversampled Black (n = 1,026) and younger women (n = 1,032). Across all studies, 36.9% of estrogen receptor (ER)-positive and 92.6% of ER-negative breast cancer had presence of at least one genomic instability signature. TP53 and HRD status were significantly associated with immune expression in both ER-positive and ER-negative breast cancer. RNA-based genomic instability signatures were associated with higher PD-L1, CD8 T-cell marker, and global and multimarker immune cell expression. Among tumors with genomic instability signatures, adaptive immune response was associated with improved recurrence-free survival regardless of ER status, highlighting genomic instability as a candidate marker for predicting immunotherapy response. Leveraging a convenient, integrated RNA-based approach, this analysis shows that genomic instability interacts with immune response, an important target in breast cancer overall and in Black women who experience higher frequency of TP53 and HR deficiency.

Significance

Despite promising advances in breast cancer immunotherapy, predictive biomarkers that are valid across diverse populations and breast cancer subtypes are needed. Genomic instability signatures can be coordinated with other RNA-based scores to define immunogenic breast cancers and may have value in stratifying immunotherapy trial participants.