Dataset Information

Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy.

ABSTRACT:

Propose

Neoadjuvant chemotherapy has been widely used in locally advanced and inflammatory breast cancer. Generally, complete pathological response after neoadjuvant chemotherapy treatment predicts survival. Studies have shown that patient-derived organoids can be used in cancer research and drug development. Therefore, we aimed to generate a living organoid biobank from biopsy samples to predict the response of patients to neoadjuvant chemotherapy.

Method

We generated a living organoid biobank from locally advanced breast cancer patients receiving neoadjuvant chemotherapy. When the patient received neoadjuvant chemotherapy, the organoids were treated with similar drugs, thereby simulating the situation of the patient receiving treatment.

Result

We successfully constructed organoids from breast cancer biopsies, demonstrating that organoids can be generated from a small sample of tissue. The phenotype of breast cancer organoid often agreed with the original breast cancer according to the blinded histopathological analysis of H&E stain tissue and organoid sections. In addition, our data confirm that the patient's response to chemotherapy closely matches the organoids' response to drugs.

Conclusion

Our data indicate that patient-derived organoids can be used to predict the clinical response of breast cancer patients to neoadjuvant chemotherapy in vitro and to screen drugs that have different effects on different patients. Key messageComplete pathological response (pCR) after adjuvant chemotherapy can predict, survival, therefore, predicting patient response to neoadjuvant chemotherapy is critical.Patient-derived organoids (PDOs) matched the original tumour in terms of histopathology, hormone receptor levels and HER2 receptor status.Patient-derived organoids can predict the responsiveness of patient to neoadjuvant chemotherapy.

SUBMITTER: Shu D

PROVIDER: S-EPMC9549797 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy.

Shu Dan D Shen Meiying M Li Kang K Han Xiaojian X Li Han H Tan Zhaofu Z Wang Yu Y Peng Yang Y Tang Zhenrong Z Qu Chi C Jin Aishun A Liu Shengchun S

Annals of medicine 20221201 1

<h4>Propose</h4>Neoadjuvant chemotherapy has been widely used in locally advanced and inflammatory breast cancer. Generally, complete pathological response after neoadjuvant chemotherapy treatment predicts survival. Studies have shown that patient-derived organoids can be used in cancer research and drug development. Therefore, we aimed to generate a living organoid biobank from biopsy samples to predict the response of patients to neoadjuvant chemotherapy.<h4>Method</h4>We generated a living or ...[more]

PMID: 36194178

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Project description:BackgroundPatient-derived xenografts (PDXs) are increasingly used in cancer research as a tool to inform cancer biology and drug response. Most available breast cancer PDXs have been generated in the metastatic setting. However, in the setting of operable breast cancer, PDX models both sensitive and resistant to chemotherapy are needed for drug development and prospective data are lacking regarding the clinical and molecular characteristics associated with PDX take rate in this setting.MethodsThe Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective neoadjuvant chemotherapy (NAC) trial of stage I-III breast cancer patients treated with neoadjuvant weekly taxane+/-trastuzumab followed by anthracycline-based chemotherapy. Using percutaneous tumor biopsies (PTB), we established and characterized PDXs from both primary (untreated) and residual (treated) tumors. Tumor take rate was defined as percent of patients with the development of at least one stably transplantable (passed at least for four generations) xenograft that was pathologically confirmed as breast cancer.ResultsBaseline PTB samples from 113 women were implanted with an overall take rate of 27.4% (31/113). By clinical subtype, the take rate was 51.3% (20/39) in triple negative (TN) breast cancer, 26.5% (9/34) in HER2+, 5.0% (2/40) in luminal B and 0% (0/3) in luminal A. The take rate for those with pCR did not differ from those with residual disease in TN (p = 0.999) and HER2+ (p = 0.2401) tumors. The xenografts from 28 of these 31 patients were such that at least one of the xenografts generated had the same molecular subtype as the patient. Among the 35 patients with residual tumor after NAC adequate for implantation, the take rate was 17.1%. PDX response to paclitaxel mirrored the patients' clinical response in all eight PDX tested.ConclusionsThe generation of PDX models both sensitive and resistant to standard NAC is feasible and these models exhibit similar biological and drug response characteristics as the patients' primary tumors. Taken together, these models may be useful for biomarker discovery and future drug development.

Project description:ImportanceImage-guided breast biopsy of a residual imaging abnormality or tumor bed after neoadjuvant chemotherapy (NACT) is increasingly used to assess residual cancer, facilitate risk-adaptive surgery, and potentially identify exceptional responders in whom local therapy may be de-escalated.ObjectiveTo further assess the accuracy of post-NACT image-guided biopsy to predict residual cancer in the breast.Design, setting, and participantsThis diagnostic study analyzed multicenter patient-level data of patients with breast cancer who were treated with NACT and underwent image-guided biopsy before surgery at Royal Marsden Hospital in London, UK; Seoul National University Hospital in Seoul, South Korea; and MD Anderson Cancer Center in Houston, Texas. Data were analyzed from June to July 2019.Main outcomes and measuresDiagnostic accuracy of post-NACT image-guided biopsy. Final surgical pathology was used as reference standard.ResultsData from 166 women were analyzed. The median (range) age was 49 (25-76) years. The median (range) tumor size on pretreatment and posttreatment imaging was 33.5 (12-100) mm and 10 (0-100) mm, respectively. The overall pathologic complete response rate was 51.2% (n = 85) (16.1% [5 of 31] for hormone receptor-positive/ERBB2 (formerly HER2)-negative; 44.7% [21 of 47] for hormone receptor-positive/ERBB2-positive; 69% [20 of 29] for hormone receptor-negative/ERBB2-positive; and 66.1% [39 of 59] for triple negative). The majority (143 [86.1%]) underwent image-guided vacuum-assisted biopsy (VAB), and 23 had core-cut biopsy. The median (range) needle gauge was 10 (7-14), and the median (range) number of samples was 6 (2-18). When image-guided biopsy (VAB and core-cut biopsy) was representative (159 [95.8%]), the false-negative rate across the whole cohort was 18.7% (95% CI, 10.6%-29.3%). Subgroup analysis of patients with a complete/partial clinical response and residual imaging abnormality of 2 cm or smaller with at least 6 VABs taken (76 [45.8%]) demonstrated a false-negative rate of 3.2% (95% CI, 0.1%-16.7%), a negative predictive value of 97.4% (95% CI, 86.5%-99.9%), and an overall accuracy of 89.5% (95% CI, 80.3%-95.3%).Conclusions and relevanceThis large multicenter pooled data analysis suggests that a standardized protocol using image-guided VAB of a tumor bed measuring 2 cm or smaller with 6 or more representative samples allows reliable prediction of residual disease. These results could inform the design of de-escalation trials in NACT exceptional responders testing the safety of eliminating surgery.

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.

Dataset Information

Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy.

Propose

Method

Result

Conclusion

Publications

Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy.

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