Background:‘TCHL’ an ICORG phase-II neo-adjuvant study in HER2+ve Breast cancer (BC) assessed the impact of TCH (Docetaxel, Carboplatin, Trastuzumab) versus TCHL (Docetaxel, Carboplatin, Trastuzumab, Lapatinib) in 78 patients. In these patients we determined the impact of total infiltrating lymphocytes (stromal and tumoral) (TIL) as a prognostic marker of pathological complete response (pCR). Methods: We determined TIL counts using immunohistochemical staining with Haemotoxalyin+Eosin, AE1/AE3 and CD45 in formalin fixed paraffin embedded (FFPE) baseline biopsy samples (n = 78) and in fresh frozen (FF) biopsies taken 20-days post cycle 1 (Day-20) of TCH/TCHL (n = 24). RNAseq analysis was performed in matched FF tumour biopsies taken pre-treatment and at Day-20 of treatment and analysed using ESTIMATE and CIBERSORT, to determine tumour content and specific immune cell subpopulations in each patient tumour sample. Results: Overall Mean TIL counts in the baseline pre-treatment biopsy samples were 33% higher in those patients who had a pCR relative to those who had either a partial response (PR) or no response (NR) (TIL pCR = 45 ± 7 vs no pCR = 34 ± 7; p = 0.15). We also assessed Mean TIL counts in FF Day-21 biopsies. In 3 patients who had no response, TIL counts (TIL count = 38 ± 16) were lower than those observed in 7 patients who had a PR (TIL count = 57 ± 6) or 3 patient who had a pCR (TIL count = 85 ± 11), with those patients who had a pCR having significantly greater TIL counts than those with a PR (p = 0.033). ESTIMATE analysis demonstrated a good correlation with tumour cell and lymphocyte counts determined by pathological review. CIBERSORT analysis reveals that those with higher plasma cell numbers in the pre-treatment samples are more likely to have a PR/pCR relative to NR patients Conclusions: TIL counts during treatment play an important role in determining the likelihood of pCR in HER2-Positive BC patients who receive neoadjuvant trastuzumab; further analysis of the role of TIL subpopulations may identify key predictive biomarkers.