Steroid receptor-associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies

Characterization of Wnt Signaling Pathway Aberrations in Metastatic Prostate Cancer

Wnt (wingless-type) signaling pathway (WSP) alterations have been identified in patients with prostate cancer and are implicated in disease progression and hormonal resistance. In this study, we utilized a multi-institutional dataset to characterize molecular alterations in the canonical and noncanonical WSPs in prostate cancer. Patients with prostate cancer who underwent tissue-based genomic sequencing were investigated. Tumors with somatic activating mutations in CTNNB1 or RSPO2 or inactivating mutations in either APC or RNF43 were characterized as having aberrant canonical Wnt signaling (WSP-activated). Overall survival analyses were restricted to microsatellite-stable (MSS) tumors lacking RNF43 G659fs* mutations. We also investigated noncanonical WSP by evaluation of ROR1, ROR2, and WNT5 in WSP-activated versus WSP wild-type (WSP-WT) tumors. Of 4,138 prostate cancer samples, 3,684 were MSS. Among MSS tumors, 42.4% were from metastatic sites, of which 19.1% were WSP activated, and 57.6% were from the prostate, of which 10.1% were WSP activated. WSP-activated tumors were more prevalent in metastatic sites than in primary prostate cancer. WSP-activated prostate cancer exhibited more SPOP mutations and higher expression of canonical WSP activators than WSP-WT tumors. ROR1 gene expression was elevated in WSP-activated tumors from both primary and metastatic sites. M2 macrophages predominated the tumor microenvironment in WSP-activated tumors. There was no significant difference in overall survival between patients with WSP-activated and WSP-WT prostate cancer. WSP-activated prostate cancer demonstrated a more immunosuppressed tumor microenvironment and a pronounced upregulation of ROR1 gene expression, underscoring its potential involvement in the crosstalk between canonical and noncanonical WSPs. Implications: Our findings may provide a rationale for developing novel therapeutic strategies targeting Wnt-activated prostate cancer.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Steroid receptor-associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies

BACKGROUND

Steroid receptor-associated and regulated protein (SRARP) has recently been identified as a novel tumor suppressor in malignancies of multiple tissue origins. SRARP is located on chromosome 1p36.13 and is widely inactivated by deletions and epigenetic silencing in malignancies. Therefore, additional studies are required to explore SRARP as a potential cancer biomarker.

AIM

This study explores the application of SRARP as a novel biomarker in malignancies of multiple tissue origins using the analysis of large genomic datasets.

METHODS AND RESULTS

A comprehensive genomic analysis of large cancer datasets was carried out to examine the association of SRARP expression and copy-number with molecular and clinical features in malignancies of multiple tissue origins. This study demonstrated that SRARP under-expression and copy-number loss are strongly associated with the loss of other tumor suppressors such as TP53 and NF1 mutations and oncogenic gains, including N-MYC amplification and ERG rearrangement, suggesting that SRARP inactivation is associated with wider genomic instability in malignancies. Importantly, SRARP under-expression and copy-number loss are strong predictors of poor clinical and/or pathological features in breast, colorectal, lung, prostate, gastric, endometrial, cervical, brain, ovarian, bladder, thyroid, and hepatocellular cancers as well as neuroblastoma, uveal melanoma, and acute myeloid leukemia with highly significant odds ratios. Finally, higher SRARP expression and copy-number predict a better response to several cancer drugs.

CONCLUSION

This study suggests that the SRARP inactivation presents a robust biomarker in predicting molecular and clinicopathological features, and treatment response in malignancies.