Functional proteomics characterization of residual breast cancer after neoadjuvant systemic chemotherapy

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Targeting CXCR4 abrogates resistance to trastuzumab by blocking cell cycle progression and synergizes with docetaxel in breast cancer treatment

BACKGROUND

Although trastuzumab and other HER2-targeted therapies have significantly improved survival in patients with HER2 overexpressed or amplified (HER2+) breast cancer, a significant proportion of patients do not respond or eventually develop clinical resistance. Strategies to reverse trastuzumab resistance remain a high clinical priority. We were the first to report the role of CXCR4 in trastuzumab resistance. The present study aims to explore the therapeutic potential of targeting CXCR4 and better understand the associated mechanisms.

METHODS

Immunofluorescent staining, confocal microscopy analysis, and immunoblotting were used to analyze CXCR4 expression. BrdU incorporation assays and flow cytometry were used to analyze dynamic CXCR4 expression. Three-dimensional co-culture (tumor cells/breast cancer-associated fibroblasts/human peripheral blood mononuclear cells) or antibody-dependent cellular cytotoxicity assay was used to mimic human tumor microenvironment, which is necessary for testing therapeutic effects of CXCR4 inhibitor or trastuzumab. The FDA-approved CXCR4 antagonist AMD3100, trastuzumab, and docetaxel chemotherapy were used to evaluate therapeutic efficacy in vitro and in vivo. Reverse phase protein array and immunoblotting were used to discern the associated molecular mechanisms.

RESULTS

Using a panel of cell lines and patient breast cancer samples, we confirmed CXCR4 drives trastuzumab resistance in HER2+ breast cancer and further demonstrated the increased CXCR4 expression in trastuzumab-resistant cells is associated with cell cycle progression with a peak in the G2/M phases. Blocking CXCR4 with AMD3100 inhibits cell proliferation by downregulating mediators of G2-M transition, leading to G2/M arrest and abnormal mitosis. Using a panel of trastuzumab-resistant cell lines and an in vivo established trastuzumab-resistant xenograft mouse model, we demonstrated that targeting CXCR4 with AMD3100 suppresses tumor growth in vitro and in vivo, and synergizes with docetaxel.

CONCLUSIONS

Our findings support CXCR4 as a novel therapeutic target and a predictive biomarker for trastuzumab resistance in HER2+ breast cancer.

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes

Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.

Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.

Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.

Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.

Targeting CXCR4 abrogates resistance to trastuzumab by blocking cell cycle progression and synergizes with docetaxel in breast cancer treatment

Background: Although trastuzumab and other HER2-targeted therapies have significantly improved survival in patients with HER2 overexpressed or amplified (HER2+) breast cancer, a significant proportion of patients do not respond or eventually develop clinical resistance. Strategies to reverse trastuzumab resistance remain a high clinical priority. We were the first to report the role of CXCR4 in trastuzumab resistance. The present study aims to explore the therapeutic potential of targeting CXCR4 and better understand the associated mechanisms. Methods: Immunofluorescent staining, confocal microscopy analysis, and immunoblotting were used to analyze CXCR4 expression. BrdU incorporation assays and flow cytometry were used to analyze dynamic CXCR4expression. Three-dimensional co-culture (tumor cells/ breast cancer-associated fibroblasts / human peripheral blood mononuclear cells) or antibody-dependent cellular cytotoxicity assay was used to mimic human tumor microenvironment, which is necessary for testing therapeutic effect of CXCR4 inhibitor or trastuzumab. The FDA-approved CXCR4 antagonist AMD3100, trastuzumab, and docetaxel chemotherapy were used to evaluate therapeutic efficacy in vitro and in vivo. Reverse phase protein array and immunoblotting were used to discern the associated molecular mechanisms. Results: Using multiple cell lines and patient breast cancer samples we confirmed CXCR4 drives trastuzumab resistance in HER2+ breast cancer and further demonstrated that the increased CXCR4 expression in trastuzumab-resistant cells is associated with cell cycle progression with a peak in the G2/M phases. Blocking CXCR4 with AMD3100 inhibits cell proliferation by downregulating mediators of G2-M transition, leading to G2/M arrest and abnormal mitosis. Using multiple trastuzumab-resistant cell lines and an in vivo established trastuzumab-resistant xenograft mouse model, we demonstrated that targeting CXCR4 with AMD3100 suppresses tumor growth in vitro and in vivo, and synergizes with docetaxel. Conclusions: Our findings support CXCR4 as a novel therapeutic target and a predictive biomarker for trastuzumab resistance in HER2+ breast cancer.

Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma

Ovarian cancer is a highly lethal gynecological malignancy. Drug resistance rapidly occurs, and different therapeutic approaches are needed. So far, no biomarkers have been discovered to predict early response to therapies in the case of multi-treated ovarian cancer patients. The aim of our investigation was to identify a protein panel and the molecular pathways involved in chemotherapy response through a combination of studying proteomics and network enrichment analysis by considering a subset of samples from a clinical setting. Differential mass spectrometry studies were performed on 14 serum samples from patients with heavily pretreated platinum-resistant ovarian cancer who received the FOLFOX-4 regimen as a salvage therapy. The serum was analyzed at baseline time (T0) before FOLFOX-4 treatment, and before the second cycle of treatment (T1), with the aim of understanding if it was possible, after a first treatment cycle, to detect significant proteome changes that could be associated with patients responses to therapy. A total of 291 shared expressed proteins was identified and 12 proteins were finally selected between patients who attained partial response or no-response to chemotherapy when both response to therapy and time dependence (T0, T1) were considered in the statistical analysis. The protein panel included APOL1, GSN, GFI1, LCATL, MNA, LYVE1, ROR1, SHBG, SOD3, TEC, VPS18, and ZNF573. Using a bioinformatics network enrichment approach and metanalysis study, relationships between serum and cellular proteins were identified. An analysis of protein networks was conducted and identified at least three biological processes with functional and therapeutic significance in ovarian cancer, including lipoproteins metabolic process, structural component modulation in relation to cellular apoptosis and autophagy, and cellular oxidative stress response. Five proteins were almost independent from the network (LYVE1, ROR1, TEC, GFI1, and ZNF573). All proteins were associated with response to drug-resistant ovarian cancer resistant and were mechanistically connected to the pathways associated with cancer arrest. These results can be the basis for extending a biomarker discovery process to a clinical trial, as an early predictive tool of chemo-response to FOLFOX-4 of heavily treated ovarian cancer patients and for supporting the oncologist to continue or to interrupt the therapy.

Post-neoadjuvant treatment and the management of residual disease in breast cancer: state of the art and perspectives

Achieving a pathologic complete response after neoadjuvant treatment is associated with improved prognosis in breast cancer. The CREATE-X trial demonstrated a significant survival improvement with capecitabine in patients with residual invasive disease after neoadjuvant chemotherapy, and the KATHERINE trial showed a significant benefit of trastuzumab-emtansine (TDM1) in human epidermal growth factor receptor 2 (HER2)-positive patients who did not achieve a pathologic complete response after neoadjuvant treatment, creating interesting alternatives of post-neoadjuvant treatments for high-risk patients. New agents are arising as therapeutic options for metastatic breast cancer such as the cyclin-dependent kinase inhibitors and the immune-checkpoint inhibitors, but none has been incorporated into the post-neoadjuvant setting so far. Evolving techniques such as next-generation sequencing and gene expression profiles have improved our knowledge regarding the biology of residual disease, and also on the mechanisms involved in treatment resistance. The present manuscript reviews the current available strategies, the ongoing trials, the potential biomarker-guided approaches and the perspectives for the post-neoadjuvant treatment and the management of residual disease after neoadjuvant treatment in breast cancer.

Profiling of Invasive Breast Carcinoma Circulating Tumour Cells-Are We Ready for the 'Liquid' Revolution?

As dissemination through blood and lymph is the critical step of the metastatic cascade, circulating tumour cells (CTCs) have attracted wide attention as a potential surrogate marker to monitor progression into metastatic disease and response to therapy. In patients with invasive breast carcinoma (IBC), CTCs are being considered nowadays as a valid counterpart for the assessment of known prognostic and predictive factors. Molecular characterization of CTCs using protein detection, genomic and transcriptomic panels allows to depict IBC biology. Such molecular profiling of circulating cells with increased metastatic abilities appears to be essential, especially after tumour resection, as well as in advanced disseminated disease, when information crucial for identification of therapeutic targets becomes unobtainable from the primary site. If CTCs are truly representative of primary tumours and metastases, characterization of the molecular profile of this easily accessible ‘biopsy’ might be of prime importance for clinical practice in IBC patients. This review summarizes available data on feasibility and documented benefits of monitoring of essential IBC biological features in CTCs, with special reference to multifactorial proteomic, genomic, and transcriptomic panels of known prognostic or predictive value.

E-cadherin loss induces targetable autocrine activation of growth factor signalling in lobular breast cancer

Despite the fact that loss of E-cadherin is causal to the development and progression of invasive lobular carcinoma (ILC), options to treat this major breast cancer subtype are limited if tumours develop resistance to anti-oestrogen treatment regimens. This study aimed to identify clinically targetable pathways that are aberrantly active downstream of E-cadherin loss in ILC. Using a combination of reverse-phase protein array (RPPA) analyses, mRNA sequencing, conditioned medium growth assays and CRISPR/Cas9-based knock-out experiments, we demonstrate that E-cadherin loss causes increased responsiveness to autocrine growth factor receptor (GFR)-dependent activation of phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/Akt signalling. Autocrine activation of GFR signalling and its downstream PI3K/Akt hub was independent of oncogenic mutations in PIK3CA, AKT1 or PTEN. Analyses of human ILC samples confirmed growth factor production and pathway activity. Pharmacological inhibition of Akt using AZD5363 or MK2206 resulted in robust inhibition of cell growth and survival of ILC cells, and impeded tumour growth in a mouse ILC model. Because E-cadherin loss evokes hypersensitisation of PI3K/Akt activation independent of oncogenic mutations in this pathway, we propose clinical intervention of PI3K/Akt in ILC based on functional E-cadherin inactivation, irrespective of activating pathway mutations.

Effects of CDK4/6 Inhibition in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer Cells with Acquired Resistance to Paclitaxel

Among patients with hormone receptor (HR)-positive breast cancer, those with residual disease after neoadjuvant chemotherapy have a higher risk of relapse and poorer survival than those with a complete response. Previous studies have revealed a correlation between activation of cell cycle-regulating pathways in HR-positive breast cancer, particularly cyclin-dependent kinase (CDK) 4 and 6/cyclin D1 signaling, and resistance to standard therapies. Although CDK4/6 inhibition by palbociclib in combination with endocrine therapy has shown potent antiproliferative effects in HR-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer, the potential role of palbociclib in re-sensitizing chemotherapy-resistant HR-positive breast cancer is not well defined. We hypothesized that CDK4/6 inhibition by palbociclib re-sensitizes HR-positive/HER2-negative residual breast cancer to taxane-based adjuvant therapy. Using cell counting, flow cytometry, and western blotting, we evaluated the efficacy of palbociclib alone and in concurrent or sequential combination with paclitaxel in parental and paclitaxel-resistant T47D HR-positive/HER2-negative breast cancer cells. The CDK4/6 pathway was constitutively active in both parental and paclitaxel-resistant T47D cells; thus, both cell types were highly sensitive to the inhibitory effects of single-agent palbociclib on cell growth and cell cycle progression. However, palbociclib did not re-sensitize resistant cells to paclitaxel-induced G2/M arrest and cell death in any of the combinations tested. Our results suggest that CDK4/6 inhibition by palbociclib does not re-sensitize HR-positive/HER2-negative residual breast cancer to chemotherapy. Nevertheless, the fact that CDK4/6 activation remained intact in paclitaxel-resistant cells indicates that patients who have HR-positive/HER2-negative residual disease after taxane-based neoadjuvant chemotherapy may still benefit from palbociclib in combination with other regimens, such as endocrine therapies, for adjuvant therapy.

Phase II Randomized Study of Ixabepilone Versus Observation in Patients With Significant Residual Disease After Neoadjuvant Systemic Therapy for HER2-Negative Breast Cancer

BACKGROUND

Residual disease (RD) after neoadjuvant chemotherapy carries an increased risk for recurrence. Ixabepilone has activity in anthracycline/taxanes-resistant breast cancer. We explored adjuvant ixabepilone in patients with significant RD HER2-negative breast cancer.

METHODS

A phase II study in patients with residual cancer burden II or III randomized to ixabepilone versus observation was conducted. Circulating tumor cells (CTCs) were measured at baseline and at 9 and 18 weeks. Survival probabilities were estimated by Kaplan-Meier product limit. Toxicities were reported as proportions in the ixabepilone arm.

RESULTS

Accrual was stopped because of ixabepilone toxicity. Sixty-seven patients were registered; 43 were randomized, 19 received ixabepilone, and 24 went to observation. One patient (9.1%) in the observation arm versus 2 patients (18.2%) in the ixabepilone arm had CTCs at 18 weeks (P = 1.0). Three-year recurrence-free survival and overall survival were 94% and 82%, and 100% and 79% in the observation and ixabepilone arms (P = .35 and .18), respectively. Most common adverse events (AEs) included fatigue, pain, neuropathy, constipation, nausea, rash, anorexia, and diarrhea. Serious AEs included pain (63.2%), fatigue (31.6%), and neuropathy (31.6%).

CONCLUSIONS

Adjuvant ixabepilone in patients with significant RD after neoadjuvant chemotherapy was difficult to administer because of AEs and did not change the presence of CTC or affect survival outcomes. NCT00877500.

Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer

Our understanding of breast cancer heterogeneity at the protein level is limited despite proteins being the ultimate effectors of cellular functions. We investigated the heterogeneity of breast cancer (41 primary tumors and 15 breast cancer cell lines) at the protein and phosphoprotein levels to identify activated oncogenic pathways and developing targeted therapeutic strategies. Heterogeneity was observed not only across histological subtypes, but also within subtypes. Tumors of the Triple negative breast cancer (TNBC) subtype distributed across four different clusters where one cluster (cluster ii) showed high deregulation of many proteins and phosphoproteins. The majority of TNBC cell lines, particularly mesenchymal lines, resembled the cluster ii TNBC tumors. Indeed, TNBC cell lines were more sensitive than non-TNBC cell lines when treated with targeted inhibitors selected based on upregulated pathways in cluster ii. In line with the enrichment of the upregulated pathways with onco-clients of Hsp90, we found synergy in combining Hsp90 inhibitors with several kinase inhibitors, particularly Erk5 inhibitors. The combination of Erk5 and Hsp90 inhibitors was effective in vitro and in vivo against TNBC leading to upregulation of pro-apoptotic effectors. Our studies contribute to proteomic profiling and improve our understanding of TNBC heterogeneity to provide therapeutic opportunities for this disease.

Implications of functional proteomics in breast cancer

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Differences in gene and protein expression and the effects of race/ethnicity on breast cancer subtypes

BACKGROUND

Differences in gene or protein expression patterns between breast cancers according to race/ethnicity and cancer subtype.

METHODS

Transcriptional profiling was performed using Affymetrix HG-U133A platform in 376 patients and reverse phase protein array analysis (RPPA) was done for 177 proteins in 255 patients from a separate cohort. Unsupervised clustering was conducted, as well as supervised comparison by race and tumor subtype. Standard statistical methods, BRB-Array tools, and Ingenuity Pathways software packages were used to analyze the data.

RESULTS

Median age was 50 years in both the cohorts. In the RPPA cohort, 54.5% of the tumors were hormone receptor-positive (HR-positive), 20.7% HER2-positive, and 24.71% triple-negative (TNBC). One hundred and forty-seven (57.6%), 47 (18.43%), and 46 (18.1%) of the patients were White, Hispanic, and Black, respectively. Unsupervised hierarchical clustering of the protein expression data showed no distinct clusters by race (P values were 0.492, 0.489, and 0.494 for the HR-positive, HER2-positive, and TNBC tumors respectively). In the gene expression cohort, 54.2% of the tumors were HR-positive, 16.5% HER2-positive, and 29.3% TNBC. Two hundred and sixteen (57.5%), 111 (29.52%), and 32 (8.52%) patients were White, Hispanic, and Black, respectively. No probe set with a false discovery rate (FDR) of <0.05 showed an association with race by breast cancer subtype; similar results were obtained using pathway and gene set enrichment analysis methods.

CONCLUSIONS

We did not detect a significant variation in RNA or protein expression comparing different race/ethnicity groups of women with breast cancer.

IMPACT

More research on the complex network of factors that result in outcomes differences among race/ethnicities is needed.