Correlation of TILs indices generated by visual review and digital computational algorithm with outcomes from preoperative chemotherapy for TNBC in TBCRC 030.
Authors: Nader Marta Guilherme , Chu Xiangying , Mukhopadhyay Satabhisa , Abramson Vandana G , Brufsky Adam , Stringer-Reasor Erica Michelle , Dent Susan Faye , Traina Tiffany A. , Carey Lisa A. , Rimawi Mothaffar F. , Specht Jennifer M. , Miller Kathy , Santa-Maria Cesar Augusto , Dasgupta Tathagata , Binboğa Kurt Busem , Tayob Nabihah , Krop Ian E. , Tung Nadine M. , Schnitt Stuart J. , Mayer Erica L. 
Published: May 2024

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Background: Predictive biomarkers for response to neoadjuvant chemotherapy (NAC) in early-stage triple negative breast cancer (TNBC) are needed. Stromal tumor infiltrating lymphocytes (TILs) are prognostic and predictive in TNBC, typically scored by visual examination (VE) of H&E stained slides. Evaluation of image-based surrogate signatures for TILs and other immune profiles by computational assessment (CA) of slides may offer greater precision and more comprehensive assessment of the immune microenvironment. This study was designed to evaluate TILs by VE and CA methods and investigate through blinded assessment the predictive capacity of immune activation. Methods: The phase II study TBCRC 030 randomized patients (pts) with BRCA1/2-proficient stage I-III TNBC to cisplatin (C) or paclitaxel (T) NAC, with primary endpoint response at surgery. TILs were visually scored on a continuous scale by a single experienced pathologist on digitized H&E images of pre-treatment core needle biopsies (CNB). CA used the 4D QPOR platform’s statistical physics and tumor biology based algorithm to quantify cell cycle aberrations and microenvironment dynamics including TILs, immune response (IHI), and combined immune response and cell cycle signature (CmbI) as continuous biomarkers from digitized CNB images. Evaluation of TILs by VE and CA were each correlated with response (RCB 0/1) or non-response (RCB 2/3) to NAC. Receiver operator characteristic (ROC) curves with area under the curve (AUC) measures and odds ratios (OR) with 95% confidence intervals were used to assess the predictive performance of each biomarker. Results: Of 139 evaluable pts, 121 had usable data for TILs by both VE and CA (59 received C, 62 received T). Descriptive statistics for biomarkers by response are shown in the Table. Notably TILs score by VE was significantly predictive of response, as was CA CmbI. TILs by VE had OR 1.86 (95% CI 1.24, 2.87, p=0.031) and AUC 0.69 (95% CI 0.57, 0.66), whereas CA CmbI had OR 1.54 (95% CI 1.00, 2.42, p=0.053) and AUC 0.62 (95% CI 0.51, 0.73). CA TILs and IHI were not predictive of response. TILs by VE predicted response to T (OR 2.91 95% CI 1.56, 6.16, p=0.002) but not C (OR 1.22 95% CI 0.67, 2.18 p=0.49). Conclusions: In this analysis of TBCRC 030, VE of baseline TILs and CA CmbI were predictive of response to NAC for TNBC, however CA did not outperform VE. TILs predicted response to specific chemotherapy. Automated computational evaluation of TILs or the broader immune microenvironment is an emerging field which may allow accurate and reproducible assessment of these important biomarkers. Clinical trial information: NCT01982448 . [Table: see text]