Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial

Abstract

BackgroundWe have previously shown lymphocyte density, measured using computational pathology, is associated with pathological complete response (pCR) in breast cancer. The clinical validity of this finding in independent studies, among patients receiving different chemotherapy, is unknown.Patients and methodsThe ARTemis trial randomly assigned 800 women with early stage breast cancer between May 2009 and January 2013 to three cycles of docetaxel, followed by three cycles of fluorouracil, epirubicin and cyclophosphamide once every 21 days with or without four cycles of bevacizumab. The primary endpoint was pCR (absence of invasive cancer in the breast and lymph nodes). We quantified lymphocyte density within haematoxylin and eosin (H&E) whole slide images using our previously described computational pathology approach: for every detected lymphocyte the average distance to the nearest 50 lymphocytes was calculated and the density derived from this statistic. We analyzed both pre-treatment biopsies and post-treatment surgical samples of the tumour bed.ResultsOf the 781 patients originally included in the primary endpoint analysis of the trial, 609 (78%) were included for baseline lymphocyte density analyses and a subset of 383 (49% of 781) for analyses of change in lymphocyte density. The main reason for loss of patients was the availability of digitized whole slide images. Pre-treatment lymphocyte density modelled as a continuous variable was associated with pCR on univariate analysis (odds ratio [OR], 2.92; 95% CI, 1.78–4.85; P < 0.001) and after adjustment for clinical covariates (OR, 2.13; 95% CI, 1.24–3.67; P = 0.006). Increased pre- to post-treatment lymphocyte density showed an independent inverse association with pCR (adjusted OR, 0.1; 95% CI, 0.033–0.31; P < 0.001).ConclusionsLymphocyte density in pre-treatment biopsies was validated as an independent predictor of pCR in breast cancer. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.ClinicalTrials.govNCT01093235.

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

“…Following this, it is possible to generate predictive or prognostic assays by applying traditional statistical frameworks (eg, Cox regression models) or machine learning classifiers of the digital pathology features to find associations to ground truth labels (eg, clinical and survival endpoints, or tissue-type classification). The Cambridge group has recently reported results using digitized pretreatment tumor biopsies and post-treatment surgical specimens obtained from the ARTemis trial [22] and from the Neo-tAnGo trial [23]. The primary aim of those studies was to investigate pathomic markers of NAC response in breast cancer, which included 1,223 baseline samples.…”

“…The primary aim of those studies was to investigate pathomic markers of NAC response in breast cancer, which included 1,223 baseline samples. Machine learning algorithms using a k-NN and support vector machine learning was used to classify tumor, stroma, and lymphocytes of the digitized pathology samples [22,23]. The results showed that lymphocyte density, as measured in the pre-treatment biopsy, was an independent predictor of pathological complete response (pCR); defined as a complete disappearance of invasive cancer cells after treatment (odds ratio ¼ 2.92-4.46, P < .001) [22].…”

“…Machine learning algorithms using a k-NN and support vector machine learning was used to classify tumor, stroma, and lymphocytes of the digitized pathology samples [22,23]. The results showed that lymphocyte density, as measured in the pre-treatment biopsy, was an independent predictor of pathological complete response (pCR); defined as a complete disappearance of invasive cancer cells after treatment (odds ratio ¼ 2.92-4.46, P < .001) [22]. Other research from Romo-Bucheli et al (2016) [24] have used deep neural networks to identify tubule-nuclei structures from digitized whole-slide images (n ¼ 174) of ER-positive breast tumors.…”

Combining measures of immune infiltration shows additive effect on survival prediction in high-grade serous ovarian carcinoma

“…7 Moreover, cell densities of lymphocytes, macrophages and dendritic cells have also been shown to be prognostic in breast, ovarian and lung cancer without restricting analyses to malignant epithelial cell areas. [8][9][10] Approaches that combine analyses of multiple immune infiltrates, such as the CD3/CD8 immunoscore in colorectal cancer, 11 have not yet been developed for ovarian cancer prognosis.…”

Update Mammakarzinom 2019 Teil 3 – aktuelle Entwicklungen bei der kurativen Behandlung von Mammakarzinom-Patientinnen: Eine Übersicht und Bewertung durch ein internationales Expertenpanel

“…[52]. Auch in anderen Studien konnte der Zusammenhang von TILs mit dem Ansprechen auf eine neoadjuvante Chemotherapie gezeigt werden [53][54][55]. In einigen Studien in der adjuvanten Therapiesituation konnte ebenfalls ein prognostischer Effekt gezeigt werden [56].…”