Systematic Evaluation of HLA-G 3’Untranslated Region Variants in Locally Advanced, Non-Metastatic Breast Cancer Patients: UTR-1, 2 or UTR-4 are Predictors for Therapy and Disease Outcome

Despite major improvements in diagnostics and therapy in early as well as in locally advanced breast cancer (LABC), metastatic relapse occurs in about 20% of patients, often explained by early micro-metastatic spread into bone marrow by disseminated tumor cells (DTC). Although neoadjuvant chemotherapy (NACT) has been a successful tool to improve overall survival (OS), there is growing evidence that various environmental factors like the non-classical human leukocyte antigen-G (HLA-G) promotes cancer invasiveness and metastatic progression. HLA-G expression is associated with regulatory elements targeting certain single-nucleotide polymorphisms (SNP) in the HLA-G 3’ untranslated region (UTR), which arrange as haplotypes. Here, we systematically evaluated the impact of HLA-G 3’UTR polymorphisms on disease status, on the presence of DTC, on soluble HLA-G levels, and on therapy and disease outcome in non-metastatic LABC patients. Although haplotype frequencies were similar in patients (n = 142) and controls (n = 204), univariate analysis revealed that the UTR-7 haplotype was related to patients with low tumor burden, whereas UTR-4 was associated with tumor sizes >T1. Furthermore, UTR-4 was associated with the presence of DTC, but UTR-3 and UTR-7 were related to absence of DTC. Additionally, increased levels of soluble HLA-G molecules were found in patients carrying UTR-7. Regarding therapy and disease outcome, univariate and multivariate analysis highlighted UTR-1 or UTR-2 as a prognostic parameter indicative for a beneficial course of disease in terms of complete response towards NACT or progression-free survival (PFS). At variance, UTR-4 was an independent risk factor for a reduced OS besides already known parameters. Taken into account the most common HLA-G 3’UTR haplotypes (UTR-1–UTR-7, UTR-18), deduction of the UTR-1/2/4 haplotypes to specific SNPs revealed that the +3003C variant, unique for UTR-4, seemed to favor a detrimental disease outcome, while the +3187G and +3196G variants, unique for UTR-1 or UTR-2, were prognostic parameters for a beneficial course of disease. In conclusion, these data suggest that the HLA-G 3’UTR variants +3003C, +3187G, and +3196G are promising candidates for the prediction of therapy and disease outcome in LABC patients.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

BackgroundSoft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS.MethodsWe retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS).ResultsThirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients’ age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies.ConclusionICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

Anthocyanidins Inhibit Growth and Chemosensitize Triple-Negative Breast Cancer via the NF-κB Signaling Pathway

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Due to the lack of drug-targetable receptors, chemotherapy is the only systemic treatment option. Although chemotherapeutic drugs respond initially in TNBC, many patients relapse and have a poor prognosis. Poor survival after metastatic relapse is largely attributed to the development of resistance to chemotherapeutic drugs. In this study, we show that bilberry-derived anthocyanidins (Anthos) can inhibit the growth and metastasis of TNBC and chemosensitize paclitaxel (PAC)-resistant TNBC cells by modulating the NF-κB signaling pathway, as well as metastatic and angiogenic mediators. Anthos administered orally significantly decreased MDA-MB-231 orthoxenograft tumor volume and led to lower rates of lymph node and lung metastasis, compared to control. Treatment of PAC-resistant MDA-MB-231Tx cells with Anthos and PAC in combination lowered the IC50 of PAC by nearly 20-fold. The combination treatment also significantly (p < 0.01) decreased the tumor volume in MDA-MB-231Tx orthoxenografts, compared to control. In contrast, Anthos and PAC alone were ineffective against MDA-MB-231Tx tumors. Our approach of using Anthos to inhibit the growth and metastasis of breast cancers, as well as to chemosensitize PAC-resistant TNBC, provides a highly promising and effective strategy for the management of TNBC.

Early Detection of Circulating Tumor DNA Postoperatively Enables Discovery of Resectable Metastatic Disease in a Patient with Colon Cancer

Currently, serum carcinoembryonic agent (CEA) along with contrast-enhanced imaging and colonoscopy are used for evaluation of recurrence of colorectal cancer. However, CEA is an unreliable and nonspecific biomarker that may fail to rise and signal relapse. Analysis of circulating tumor DNA (ctDNA) in patients offers a minimally invasive method to assess risk of relapse several months ahead of conventional clinical means. Here, we report the case of a colon adenocarcinoma with postoperative liver metastasis diagnosed early by ctDNA measurement, using a personalized NGS-mPCR assay. While ctDNA levels continued to rise, CEA levels tested negative. Metastatic relapse to the liver was promptly confirmed by PET/CT scan. The patient underwent a successful metastasectomy with curative intent. Following surgery, the patient exhibited no evidence of disease and ctDNA levels remained negative. Our case report suggests that the early detection of postoperative molecular residual disease by means of ctDNA measurement can accurately predict mCRC relapse in cases where CEA levels fail to increase. Close monitoring of ctDNA levels during the postoperative period can allow for earlier intervention and more favorable outcomes in relapsing mCRC patients.

Medium levels of transcription and replication related chromosomal instability are associated with poor clinical outcome

Genomic instability (GI) influences treatment efficacy and resistance, and an accurate measure of it is lacking. Current measures of GI are based on counts of specific structural variation (SV) and mutational signatures. Here, we present a holistic approach to measuring GI based on the quantification of the steady-state equilibrium between DNA damage and repair as assessed by the residual breakpoints (BP) remaining after repair, irrespective of SV type. We use the notion of Hscore, a BP “hotspotness” magnitude scale, to measure the propensity of genomic structural or functional DNA elements to break more than expected by chance. We then derived new measures of transcription- and replication-associated GI that we call iTRAC (transcription-associated chromosomal instability index) and iRACIN (replication-associated chromosomal instability index). We show that iTRAC and iRACIN are predictive of metastatic relapse in Leiomyosarcoma (LMS) and that they may be combined to form a new classifier called MAGIC (mixed transcription- and replication-associated genomic instability classifier). MAGIC outperforms the gold standards FNCLCC and CINSARC in stratifying metastatic risk in LMS. Furthermore, iTRAC stratifies chemotherapeutic response in LMS. We finally show that this approach is applicable to other cancers.

Somatic driver mutation prevalence in 1844 prostate cancers identifies ZNRF3 loss as a predictor of metastatic relapse

Driver gene mutations that are more prevalent in metastatic, castration-resistant prostate cancer (mCRPC) than localized disease represent candidate prognostic biomarkers. We analyze 1,844 localized (1,289) or mCRPC (555) tumors and quantify the prevalence of 113 somatic driver single nucleotide variants (SNVs), copy number aberrations (CNAs), and structural variants (SVs) in each state. One-third are significantly more prevalent in mCRPC than expected while a quarter are less prevalent. Mutations in AR and its enhancer are more prevalent in mCRPC, as are those in TP53, MYC, ZNRF3 and PRKDC. ZNRF3 loss is associated with decreased ZNRF3 mRNA abundance, WNT, cell cycle & PRC1/2 activity, and genomic instability. ZNRF3 loss, RNA downregulation and hypermethylation are prognostic of metastasis and overall survival, independent of clinical and pathologic indices. These data demonstrate a strategy for identifying biomarkers of localized cancer aggression, with ZNRF3 loss as a predictor of metastasis in prostate cancer.

Redox Control of the Dormant Cancer Cell Life Cycle

Following efficient tumor therapy, some cancer cells may survive through a dormancy process, contributing to tumor recurrence and worse outcomes. Dormancy is considered a process where most cancer cells in a tumor cell population are quiescent with no, or only slow, proliferation. Recent advances indicate that redox mechanisms control the dormant cancer cell life cycle, including dormancy entrance, long-term dormancy, and metastatic relapse. This regulatory network is orchestrated mainly through redox modification on key regulators or global change of reactive oxygen species (ROS) levels in dormant cancer cells. Encouragingly, several strategies targeting redox signaling, including sleeping, awaking, or killing dormant cancer cells are currently under early clinical evaluation. However, the molecular mechanisms underlying redox control of the dormant cancer cell cycle are poorly understood and need further exploration. In this review, we discuss the underlying molecular basis of redox signaling in the cell life cycle of dormant cancer and the potential redox-based targeting strategies for eliminating dormant cancer cells.

The Roles of the Let-7 Family of MicroRNAs in the Regulation of Cancer Stemness

Cancer has long been viewed as a disease of normal development gone awry. Cancer stem-like cells (CSCs), also termed as tumor-initiating cells (TICs), are increasingly recognized as a critical tumor cell population that drives not only tumorigenesis but also cancer progression, treatment resistance and metastatic relapse. The let-7 family of microRNAs (miRNAs), first identified in C. elegans but functionally conserved from worms to human, constitutes an important class of regulators for diverse cellular functions ranging from cell proliferation, differentiation and pluripotency to cancer development and progression. Here, we review the current state of knowledge regarding the roles of let-7 miRNAs in regulating cancer stemness. We outline several key RNA-binding proteins, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) involved in the regulation of let-7 biogenesis, maturation and function. We then highlight key gene targets and signaling pathways that are regulated or mutually regulated by the let-7 family of miRNAs to modulate CSC characteristics in various types of cancer. We also summarize the existing evidence indicating distinct metabolic pathways regulated by the let-7 miRNAs to impact CSC self-renewal, differentiation and treatment resistance. Lastly, we review current preclinical studies and discuss the clinical implications for developing let-7-based replacement strategies as potential cancer therapeutics that can be delivered through different platforms to target CSCs and reduce/overcome treatment resistance when applied alone or in combination with current chemo/radiation or molecularly targeted therapies. By specifically targeting CSCs, these strategies have the potential to significantly improve the efficacy of cancer therapies.

Implementing a Machine Learning Strategy to Predict Pathologic Response in Patients With Soft Tissue Sarcomas Treated With Neoadjuvant Chemotherapy

PURPOSE Neoadjuvant chemotherapy (NAC) has been increasingly used in patients with locally advanced high-risk soft tissue sarcomas in the past decade, but definition and prognostic impact of a good histologic response (GHR) are lacking. Our aim was to investigate which histologic feature from the post-NAC surgical specimen independently correlated with metastatic relapse-free survival (MFS) in combination with clinical, radiologic, and pathologic features using a machine learning approach. METHODS This retrospective study included 175 consecutive patients (median age: 59 years, 75 women) with resectable disease, treated with anthracycline-based NAC between 1989 and 2015 in our sarcoma reference center, and with quantitative histopathologic analysis of the surgical specimen. The outcome of interest was the MFS. A multimodel, multivariate survival analysis was used to define GHR. The added prognostic value of GHR was investigated through the comparisons with the standard model (including histologic grade, size, and depth) and SARCULATOR nomogram using concordance indices (c-index) and Monte-Carlo cross-validation. RESULTS Seventy-two patients (72 of 175, 41.1%) had a metastatic relapse. Stepwise Cox regression, random survival forests, and least absolute shrinkage and selection operator–penalized Cox regression all converged toward the same definition for GHR, ie, < 5% stainable tumor cells. The five-year MFS probability was 1 (95% CI, 1 to 1) in patients with GHR versus 0.73 (95% CI, 0.65 to 0.81) in patients without GHR (log-rank P = .0122). The final prognostic model incorporating the GHR was significantly better than the standard model and SARCULATOR (average c-index in testing sets = 0.72 [95% CI, 0.61 to 0.82] v 0.57 [95% CI, 0.44 to 0.70] and 0.54 [95% CI, 0.45 to 0.64], respectively; P = .0414 and .0091). CONCLUSION Histologic response to NAC improves the prediction of MFS in patients with soft tissue sarcoma and represents a possible end point in future studies exploring innovative regimens in the neoadjuvant setting.