Correction to: NSABP FB-7: a phase II randomized neoadjuvant trial with paclitaxel + trastuzumab and/or neratinib followed by chemotherapy and postoperative trastuzumab in HER2+ breast cancer.

After the publication of this work [1] the authors have reported that in Table 3 The letter "T" in columns 5 and 7 should not be there.

NSABP FB-7: a phase II randomized neoadjuvant trial with paclitaxel + trastuzumab and/or neratinib followed by chemotherapy and postoperative trastuzumab in HER2+ breast cancer.

PURPOSE: The primary aim of NSABP FB-7 was to determine the pathologic complete response (pCR) rate in locally advanced HER2-positive (HER2+) breast cancer patients treated with neoadjuvant trastuzumab or neratinib or the combination and weekly paclitaxel followed by standard doxorubicin plus cyclophosphamide. The secondary aims include biomarker analyses. EXPERIMENTAL DESIGN: pCR was tested for association with treatment, gene expression, and a single nucleotide polymorphism (SNP) in the Fc fragment of the IgG receptor IIIa-158V/F (FCGR3A). Pre-treatment biopsies and residual tumors were also compared to identify molecular changes. RESULTS: The numerical pCR rate in the trastuzumab plus neratinib arm (50% [95%CI 34-66%]) was greater than that for single-targeted therapies with trastuzumab (38% [95%CI 24-54]) or neratinib (33% [95%CI 20-50]) in the overall cohort but was not statistically significant. Hormone receptor-negative (HR-) tumors had a higher pCR rate than HR+ tumors in all three treatment arms, with the highest pCR rate in the combination arm. Diarrhea was the most frequent adverse event and occurred in virtually all patients who received neratinib-based therapy. Grade 3 diarrhea was reported in 31% of patients; there were no grade 4 events. Our 8-gene signature, previously validated for trastuzumab benefit in two different clinical trials in the adjuvant setting, was correlated with pCR across all arms of NSABP FB-7. Specifically, patients predicted to receive no trastuzumab benefit had a significantly lower pCR rate than did patients predicted to receive the most benefit (P = 0.03). FCGR genotyping showed that patients who were homozygous for the Fc low-binding phenylalanine (F) allele for FCGR3A-158V/F were less likely to achieve pCR. CONCLUSIONS: Combining trastuzumab plus neratinib with paclitaxel increased the absolute pCR rate in the overall cohort and in HR- patients. The 8-gene signature, which is validated for predicting trastuzumab benefit in the adjuvant setting, was associated with pCR in the neoadjuvant setting, but remains to be validated as a predictive marker in a larger neoadjuvant clinical trial. HR status, and the FCGR3A-158V/F genotype, also warrant further investigation to identify HER2+ patients who may benefit from additional anti-HER2 therapies beyond trastuzumab. All of these markers will require further validation in the neoadjuvant setting. TRIALS REGISTRATION: ClinicalTrials.gov, NCT01008150. Retrospectively registered on October 5, 2010.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. METHODS: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. RESULTS: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). CONCLUSION: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Neratinib-Plus-Cetuximab in Quadruple-WT (KRAS, NRAS, BRAF, PIK3CA) Metastatic Colorectal Cancer Resistant to Cetuximab or Panitumumab: NSABP FC-7, A Phase Ib Study.

PURPOSE: In metastatic colorectal cancer (mCRC), HER2 (ERBB2) gene amplification is implicated in anti-EGFR therapy resistance. We sought to determine the recommended phase II dose (RP2D) and efficacy of neratinib, a pan-ERBB kinase inhibitor, combined with cetuximab, in patients with progressive disease (PD) on anti-EGFR treatment. PATIENTS AND METHODS: Twenty-one patients with quadruple-wild-type, refractory mCRC enrolled in this 3+3 phase Ib study. Standard dosage cetuximab was administered with neratinib at 120 mg, 160 mg, 200 mg, and 240 mg/day orally in 28-day cycles. Samples were collected for molecular and pharmacokinetic studies. RESULTS: Sixteen patients were evaluable for dose-limiting toxicity (DLT). 240 mg was determined to be the RP2D wherein a single DLT occurred (1/7 patients). Treatment-related DLTs were not seen at lower doses. Best response was stable disease (SD) in 7 of 16 (44%) patients. HER2 amplification (chromogenic in situ IHC) was detected in 2 of 21 (9.5%) treatment-naïve tumors and 4 of 16 (25%) biopsies upon trial enrollment (post-anti-EGFR treatment and progression). Compared with matched enrollment biopsies, 6 of 8 (75%) blood samples showed concordance for HER2 CNV in circulating cell-free DNA. Five SD patients had HER2 amplification in either treatment-naïve or enrollment biopsies. Examination of gene-expression, total protein, and protein phosphorylation levels showed relative upregulation of ≥2 members of the HER-family receptors or ligands upon enrollment versus matched treatment-naïve samples. CONCLUSIONS: The RP2D of neratinib in this combination was 240 mg/day, which was well tolerated with low incidence of G3 AEs. There were no objective responses; SD was seen at all neratinib doses. HER2 amplification, detectable in both tissue and blood, was more frequent post-anti-EGFR therapy.

Validation of the NSABP/NRG Oncology 8-Gene Trastuzumab-benefit Signature in Alliance/NCCTG N9831.

Our objective was to validate the NSABP 8-gene trastuzumab-benefit signature, developed and initially validated in NRG Oncology/NSABP B-31 in Alliance/NCCTG N9831. The B-31 and N9831 trials demonstrated the benefit of adding trastuzumab to chemotherapy in the adjuvant setting for HER2+ breast cancer patients. NSABP investigators utilized gene expression profiles of N9831 patients (N = 892) to blindly assign patients to large-, moderate-, or no-trastuzumab benefit groups and then NCCTG investigators assessed the degree of trastuzumab benefit using Cox models adjusted for age, nodes, estrogen receptor/progesterone receptor status, tumor size, and grade. Hazard ratios and 2-sided P values for recurrence-free survival of the predicted large- (n = 387), moderate- (n = 401), and no-benefit (n = 104) groups, based on the 8-gene signature were 0.47 (95% CI = 0.31 to 0.73, P < .001), 0.60 (95% CI = 0.39 to 0.92, P = .02), and 1.54 (95% CI = 0.59 to 4.02, P = .38), respectively (P interaction  = .02), providing validation of the 8-gene signature in an independent study.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer.

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.

Stromal Tumor-infiltrating Lymphocytes in NRG Oncology/NSABP B-31 Adjuvant Trial for Early-Stage HER2-Positive Breast Cancer.

We retrospectively assessed association of stromal tumor-infiltrating lymphocytes (sTILs) with clinical outcomes and molecular variables reportedly predictive of trastuzumab-benefit in National Surgical Adjuvant Breast and Bowel Project B-31 (N = 2130). sTILs were assessed in 1581 eligible B-31 cases utilizing all available hematoxylin and eosin slides. Mean concordance between main reviewer and six other pathologists was 90.8% in 100 cases. Cox regressions were used to calculate hazard ratios (HRs). In chemotherapy and trastuzumab added to chemotherapy arms, increases in sTILs, as a semicontinuous variable (combined arms HR = 0.42, 95% confidence interval = 0.27 to 0.64, two-sided P < .001) or as lymphocyte-predominant breast cancer with more than 50% sTILs (combined arms HR = 0.65, 95% confidence interval = 0.49 to 0.86, two-sided P = .003) were statistically significantly associated with improved disease-free survival. There was no association of sTILs with trastuzumab benefit. However, higher sTILs were statistically significantly associated with higher trastuzumab benefit groups by 8-gene prediction model (two-sided P < .001). Neither PIK3CA mutations nor Fc-gamma-receptor polymorphisms were associated with sTILs. sTILs may have utility as a prognostic biomarker identifying HER2-positive early breast cancer at low recurrence risk.

Molecular subtypes of colorectal cancer in pre-clinical models show differential response to targeted therapies: Treatment implications beyond KRAS mutations.

Molecular subtypes of colorectal tumors are associated with prognosis and prediction for treatment benefit from chemotherapy. The purpose of this study was two-fold: 1) to determine the association of colorectal (CRC) molecular subtypes with response to targeted therapies in pre-clinical models and 2) to identify treatments for CRC stem-like subtype because these tumors are associated with a very poor patient prognosis. Eleven CRC cell lines were classified into molecular subtypes and tested for their response to pan-ERBB, MEK, and ERK inhibitors as single agents and in combination. All six inflammatory or TA cell lines were exquisitely sensitive to the combination of MEK and neratinib whereas all five stem-like cell lines were resistant. Growth inhibition in sensitive cell lines was greater with the combination than with either drug alone even in cell lines with KRAS mutations. The combination inhibited pERK in inflammatory cell lines but not in four out of five stem-like cell lines. MEK162 plus neratinib were synergistic in cell culture and xenograft models in inflammatory cell lines. The ERK inhibitor, SCH772984, down-regulated pERK, decreased cell viability, and was synergistic with neratinib in both inflammatory and stem-like subtypes. These results suggest that inhibition of pERK is a critical node in decreasing cell viability of stem-like CRC tumors. Our results also suggest that CRC molecular subtypes may yield predictive information and may help to identify patients who may respond to targeted inhibitors.