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Proteomic patterns associated with response to breast cancer neoadjuvant treatment.

ABSTRACT: Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry-based proteomic approach and analyzed a breast cancer cohort of 113 formalin-fixed paraffin-embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor-adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of post-treatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ER-positive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy.

SUBMITTER: Shenoy A

PROVIDER: S-EPMC7507992 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Proteomic patterns associated with response to breast cancer neoadjuvant treatment.

Shenoy Anjana A Belugali Nataraj Nishanth N Perry Gili G Loayza Puch Fabricio F Nagel Remco R Marin Irina I Balint Nora N Bossel Noa N Pavlovsky Anya A Barshack Iris I Kaufman Bella B Agami Reuven R Yarden Yosef Y Dadiani Maya M Geiger Tamar T

Molecular systems biology 20200901 9

Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry-based proteomic approach and analyzed a breast cancer cohort of 113 formalin-fixed paraffin-embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor-adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein le ...[more]

PMID: 32960509

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Project description:BackgroundNeoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.MethodsSerial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.ResultsHighest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.ConclusionsOverall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Project description:BACKGROUND:The main purpose was to investigate the correlation between magnetic resonance imaging (MRI)-based response patterns halfway through neoadjuvant chemotherapy and immunotherapy (NAC) and pathological tumor response in patients with breast cancer. Secondary purposes were to compare the predictive value of MRI-based response patterns measured halfway through NAC and after NAC and to measure interobserver variability. METHODS:All consecutive patients treated with NAC for primary invasive breast cancer from 2012 to 2015 and who underwent breast MRI before, halfway through (and after) NAC were included. All breast tumors were reassessed on MRI by two experienced breast radiologists and classified into six patterns: type 0 (complete radiologic response); type 1 (concentric shrinkage); type 2 (crumbling); type 3 (diffuse enhancement); type 4 (stable disease); type 5 (progressive disease). Percentages of tumors showing pathological complete response (pCR), > 50% tumor reduction and > 50% tumor diameter reduction per MRI-based response pattern were calculated. Correlation between MRI-based response patterns and pathological tumor reduction was studied with Pearson's correlation coefficient, and interobserver agreement was tested with Cohen's Kappa. RESULTS:Patients (n = 76; mean age 53, range 29-72 years) with 80 tumors (4 bilateral) were included. There was significant correlation between these MRI-based response patterns halfway through NAC and tumor reduction on pathology assessment (reader 1 r = 0.33; p = 0.003 and reader 2 r = 0.45; p < 0.001). Type-0, type-1 or type-2 patterns halfway through NAC showed highest tumor reduction rates on pathology assessment, with > 50% tumor reduction in 90%, 78% and 65% of cases, respectively. In 83% of tumors with type 0 halfway through NAC, pathology assessment showed pCR. There was no significant correlation between MRI-based response patterns after NAC and tumor reduction rates on pathology assessment (reader 1 r = - 0.17; p = 0.145 and reader 2 r = - 0.17; p = 0.146). In 41% of tumors with type 0 after NAC, pathology assessment showed pCR. CONCLUSION:MRI-based response patterns halfway through NAC can predict pathologic response more accurately than MRI-based response patterns after NAC. Complete radiological response halfway NAC is associated with 83% pCR, while complete radiological response after NAC seems to be correct in only 41% of cases.

Project description:We sought to determine whether dysregulation of the retinoblastoma (RB) tumor suppressor pathway was associated with improved response to neoadjuvant chemotherapy in breast cancer.An RB-loss signature was used to analyze the association between pathway status and pathologic complete response in gene expression datasets encompassing three different neoadjuvant regimens. Parallel immunohistochemical analysis of the RB pathway was conducted on pretreatment biopsies to determine the association with pathologic response to neoadjuvant chemotherapy.An RB-loss gene expression signature was associated with increased pathologic complete response in datasets from breast cancer patients treated with 5-fluorouracil/adriamycin/cytoxan (FAC; P < 0.001), T/FAC (P < 0.001), and Taxane/Adriaymcin (P < 0.001) neoadjuvant therapy encompassing approximately 1,000 patients. The association with improved response to neoadjuvant chemotherapy was true in both estrogen receptor (ER)-positive and ER-negative breast cancer. Elevated expression of p16ink4a is associated with the RB-loss signature (R = 0.493-0.5982), and correspondingly p16ink4a mRNA levels were strongly associated with pathologic complete response in the same datasets analyzed. In an independent cohort, immunohistochemical analyses of RB and p16ink4a revealed an association of RB loss (P = 0.0018) or elevated p16ink4a (P = 0.0253) with pathologic complete response. In addition, by Miller-Payne and clinicopathologic scoring analyses, RB-deficient tumors experienced an overall improved response to neoadjuvant chemotherapy.Disruption of the RB pathway as measured by several independent methods was associated with improved response to neoadjuvant chemotherapy. The RB-pathway status was relevant for pathologic response in both ER-positive and ER-negative breast cancer with similar results observed with multiple chemotherapy regimens. Combined, these data indicate that RB status is associated with the response to neoadjuvant chemotherapy in breast cancer and could be used to inform treatment.

Project description:BackgroundNeoadjuvant chemotherapy is increasingly given preoperatively to shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone.MethodsThis study included a total of 97 samples from a cohort of 50 women (aged 29-76, with 46% ER+ and 20% HER2+ tumours) with primary operable breast cancer who had been treated with neoadjuvant chemotherapy. Biopsies were taken at diagnosis, at 2 weeks on-treatment, mid-chemotherapy, and at resection. Fresh frozen samples were sequenced with Ion AmpliSeq Transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 patients with a complete pathological response (pCR) and 34 non-pCR patients, and over treatment time to identify significantly differentially expressed genes, pathways, and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY 1 Trial.ResultsAlthough changes upon treatment are largely similar between the two cohorts, very few genes were found to be consistently different between responders and non-responders, making the prediction of response difficult. AAGAB was identified as a novel potential on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY 1 testing dataset. AAGAB levels on-treatment were also significantly predictive of outcome (p = 0.048, p = 0.0036) in both cohorts. This single gene on-treatment biomarker had greater predictive accuracy than established prognostic tests, Mammaprint and PAM50 risk of recurrence score, although interestingly, both of these latter tests performed better in the on-treatment rather than the accepted pre-treatment setting.ConclusionChanges in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a potentially novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on than before treatment. These results support the potential use and further evaluation of on-treatment testing in breast cancer to improve the accuracy of tumour response prediction.

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Proteomic patterns associated with response to breast cancer neoadjuvant treatment.

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Proteomic patterns associated with response to breast cancer neoadjuvant treatment.

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