The dysregulation of post-translational modifications (PTM) transversally impacts cancer hallmarks and constitutes an appealing vulnerability for drug development. In breast cancer there is growing preclinical evidence of the role of ubiquitin and ubiquitin-like SUMO and Nedd8 peptide conjugation to the proteome in tumorigenesis and drug resistance, particularly through their interplay with estrogen receptor signaling and DNA repair. Herein we explored genomic alterations in these processes using RNA-seq and mutation data from TCGA and METABRIC datasets, and analyzed them using a bioinformatic pipeline in search of those with prognostic and predictive capability which could qualify as subjects of drug research. Amplification of UBE2T, UBE2C, and BIRC5 conferred a worse prognosis in luminal A/B and basal-like tumors, luminal A/B tumors, and luminal A tumors, respectively. Higher UBE2T expression levels were predictive of a lower rate of pathological complete response in triple negative breast cancer patients following neoadjuvant chemotherapy, whereas UBE2C and BIRC5 expression was higher in luminal A patients with tumor relapse within 5 years of endocrine therapy or chemotherapy. The transcriptomic signatures of USP9X and USP7 gene mutations also conferred worse prognosis in luminal A, HER2-enriched, and basal-like tumors, and in luminal A tumors, respectively. In conclusion, we identified and characterized the clinical value of a group of genomic alterations in ubiquitination, SUMOylation, and neddylation enzymes, with potential for drug development in breast cancer.

Endocrine-resistant, hormone receptor-positive, and HER2-negative (HR+/HER2-) metastatic breast cancer (mBC) is largely governed by acquired mutations in the estrogen receptor, which promote ligand-independent activation, and by truncal alterations in the PI3K signaling pathway, with a broader range of gene alterations occurring with less prevalence. Circulating tumor DNA (ctDNA)-based technologies are progressively permeating the clinical setting. However, their utility for serial monitoring has been hindered by their significant costs, inter-technique variability, and real-world patient heterogeneity. We interrogated a longitudinal collection of 180 plasma samples from 75 HR+/HER2- mBC patients who progressed or relapsed after exposure to aromatase inhibitors and were subsequently treated with endocrine therapy (ET) by means of highly sensitive and affordable digital PCR and SafeSEQ sequencing. Baseline PIK3CA and TP53 mutations were prognostic of a shorter progression-free survival in our population. Mutant PIK3CA was prognostic in the subset of patients receiving fulvestrant monotherapy after progression to a CDK4/6 inhibitor (CDK4/6i)-containing regimen, and its suppression was predictive in a case of long-term benefit with alpelisib. Mutant ESR1 was prognostic in patients who did not receive concurrent CDK4/6i, an impact influenced by the variant allele frequency, and its early suppression was strongly predictive of efficacy and associated with long-term benefit in the whole cohort. Mutations in ESR1, TP53, and KRAS emerged as putative drivers of acquired resistance. These findings collectively contribute to the characterization of longitudinal ctDNA in real-world cases of HR+/HER2- mBC previously exposed to aromatase inhibitors and support ongoing studies either targeting actionable alterations or leveraging the ultra-sensitive tracking of ctDNA.

Alternative splicing is an essential biological process, which increases the diversity and complexity of the human transcriptome. In our study, 304 splicing pathway-related genes were evaluated in tumors from breast cancer patients (TCGA dataset). A high number of alterations were detected, including mutations and copy number alterations (CNAs), although mutations were less frequently present compared with CNAs. In the four molecular subtypes, 14 common splice genes showed high level amplification in >5% of patients. Certain genes were only amplified in specific breast cancer subtypes. Most altered genes in each molecular subtype clustered to a few chromosomal regions. In the Luminal subtype, amplifications of LSM1, CLNS1A, and ILF2 showed a strong significant association with prognosis. An even more robust association with OS and RFS was observed when expression of these three genes was combined. Inhibition of LSM1, CLNS1A, and ILF2, using siRNA in MCF7 and T47D cells, showed a decrease in cell proliferation. The mRNA expression of these genes was reduced by treatment with BET inhibitors, a family of epigenetic modulators. We map the presence of splicing-related genes in breast cancer, describing three novel genes, LSM1, CLNS1A, and ILF2, that have an oncogenic role and can be modulated with BET inhibitors.

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype.

Somatic variant calling is a critical step in cancer genome analysis, but the performance of available tools can vary depending on their underlying algorithms and filtering strategies. We compared three widely used variant callers—Mutect2, Strelka2, and FreeBayes—for their performance in somatic single-nucleotide variant (SNV) detection using both synthetic and real whole-exome sequencing (WES) data. Synthetic data were generated by introducing 4709 SNVs into a variant-free BAM file, while real data consisted of tumor and matched normal WES samples from five ovarian cancer (OC) patients. All callers were run using the nf-core/sarek pipeline with default settings and appropriate filtering. In the synthetic dataset, all tools showed high precision (~99.9%), with Mutect2 achieving the highest recall (63.1%), followed by Strelka2 (46.3%) and FreeBayes (45.2%). In real samples, FreeBayes detected the most variants, and only 5.1% of SNVs were shared across all three tools. We then integrated calls with SomaticSeq in consensus mode (Mutect2 + Strelka2) and kept variants with stronger allelic signals—showing higher VAFs and, typically, higher coverages relative to single-caller only. Caller-exclusive variants showed significant differences in allele frequency and sequencing depth. These results highlight substantial variability in SNV detection across tools. While all showed high specificity, differences in sensitivity and variant profiles underscore the need for context-specific caller selection or ensemble approaches in cancer genomics.