Survival Outcomes, Digital TILs, and On-treatment PET/CT During Neoadjuvant Therapy for HER2-positive Breast Cancer: Results from the Randomized PREDIX HER2 Trial

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study

PURPOSE

To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.

MATERIALS AND METHODS

In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve.

RESULTS

A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model.

CONCLUSION

The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.

Fertility and reproductive concerns related to the new generation of cancer drugs and the clinical implication for young individuals undergoing treatments for solid tumors

The treatment landscape of solid tumors has changed markedly in the last years. Molecularly targeted treatments and immunotherapies have been implemented and have, in many cancers, lowered the risk of relapse and prolonged survival. Patients with tumors harboring specific targetable molecular alterations or mutations are often of a younger age, and hence future fertility and family building can be important concerns in this group. However, there are great uncertainties regarding the effect of the new drugs on reproductive functions, including fertility, pregnancy and lactation and how young patients with cancers, both women and men should be advised. The goal with this review is to gather the current knowledge regarding oncofertility and the different novel therapies, including immune checkpoint inhibitors, antibody-drug conjugates, small molecules and monoclonal antibody targeted therapies. The specific circumstances and reproductive concerns in different patient groups where novel treatments have been broadly introduced are also discussed, including those with melanoma, lung, breast, colorectal and gynecological cancers. It is clear, that more awareness is needed regarding potential drug toxicity on reproductive tissues, and it is of essence that individuals are informed based on current expertise and on available fertility preservation methods.

3-year invasive disease-free survival with chemotherapy de-escalation using an 18F-FDG-PET-based, pathological complete response-adapted strategy in HER2-positive early breast cancer (PHERGain): a randomised, open-label, phase 2 trial

BACKGROUND

PHERGain was designed to assess the feasibility, safety, and efficacy of a chemotherapy-free treatment based on a dual human epidermal growth factor receptor 2 (HER2) blockade with trastuzumab and pertuzumab in patients with HER2-positive early breast cancer (EBC). It used an 18fluorine-fluorodeoxyglucose-PET-based, pathological complete response (pCR)-adapted strategy.

METHODS

PHERGain was a randomised, open-label, phase 2 trial that took place in 45 hospitals in seven European countries. It randomly allocated patients in a 1:4 ratio with centrally confirmed, HER2-positive, stage I-IIIA invasive, operable breast cancer with at least one PET-evaluable lesion to either group A, where patients received docetaxel (75 mg/m2, intravenous), carboplatin (area under the curve 6 mg/mL per min, intravenous), trastuzumab (600 mg fixed dose, subcutaneous), and pertuzumab (840 mg loading dose followed by 420 mg maintenance doses, intravenous; TCHP), or group B, where patients received trastuzumab and pertuzumab with or without endocrine therapy, every 3 weeks. Random allocation was stratified by hormone receptor status. Centrally reviewed PET was conducted at baseline and after two treatment cycles. Patients in group B were treated according to on-treatment PET results. Patients in group B who were PET-responders continued with trastuzumab and pertuzumab with or without endocrine therapy for six cycles, while PET-non-responders were switched to receive six cycles of TCHP. After surgery, patients in group B who were PET-responders who did not achieve a pCR received six cycles of TCHP, and all patients completed up to 18 cycles of trastuzumab and pertuzumab. The primary endpoints were pCR in patients who were group B PET-responders after two treatment cycles (the results for which have been reported previously) and 3-year invasive disease-free survival (iDFS) in patients in group B. The study is registered with ClinicalTrials.gov (NCT03161353) and is ongoing.

FINDINGS

Between June 26, 2017, and April 24, 2019, a total of 356 patients were randomly allocated (71 patients in group A and 285 patients in group B), and 63 (89%) and 267 (94%) patients proceeded to surgery in groups A and B, respectively. At this second analysis (data cutoff: Nov 4, 2022), the median duration of follow-up was 43·3 months (range 0·0-63·0). In group B, the 3-year iDFS rate was 94·8% (95% CI 91·4-97·1; p=0·001), meeting the primary endpoint. No new safety signals were identified. Treatment-related adverse events and serious adverse events (SAEs) were numerically higher in patients allocated to group A than to group B (grade ≥3 62% vs 33%; SAEs 28% vs 14%). Group B PET-responders with pCR presented the lowest incidence of treatment-related grade 3 or higher adverse events (1%) without any SAEs.

INTERPRETATION

Among HER2-positive EBC patients, a PET-based, pCR-adapted strategy was associated with an excellent 3-year iDFS. This strategy identified about a third of patients who had HER2-positive EBC who could safely omit chemotherapy.

FUNDING

F Hoffmann-La Roche.

The role of serum thymidine kinase 1 activity in neoadjuvant-treated HER2-positive breast cancer: biomarker analysis from the Swedish phase II randomized PREDIX HER2 trial

BACKGROUND

Thymidine kinase 1 (TK1) plays a pivotal role in DNA synthesis and cellular proliferation. TK1 has been studied as a prognostic marker and as an early indicator of treatment response in human epidermal growth factor 2 (HER2)-negative early and metastatic breast cancer (BC). However, the prognostic and predictive value of serial TK1 activity in HER2-positive BC remains unknown.

METHODS

In the PREDIX HER2 trial, 197 HER2-positive BC patients were randomized to neoadjuvant trastuzumab, pertuzumab, and docetaxel (DPH) or trastuzumab emtansine (T-DM1), followed by surgery and adjuvant epirubicin and cyclophosphamide. Serum samples were prospectively collected from all participants at multiple timepoints: at baseline, after cycle 1, 2, 4, and 6, at end of adjuvant therapy, annually for a total period of 5 years and/or at the time of recurrence. The associations of sTK1 activity with baseline characteristics, pathologic complete response (pCR), event-free survival (EFS), and disease-free survival (DFS) were evaluated.

RESULTS

No association was detected between baseline sTK1 levels and all the baseline clinicopathologic characteristics. An increase of TK1 activity from baseline to cycle 2 was seen in all cases. sTK1 level at baseline, after 2 and 4 cycles was not associated with pCR status. After a median follow-up of 58 months, 23 patients had EFS events. There was no significant effect between baseline or cycle 2 sTK1 activity and time to event. A non-significant trend was noted among patents with residual disease (non-pCR) and high sTK1 activity at the end of treatment visit, indicating a potentially worse long-term prognosis.

CONCLUSION

sTK1 activity increased following neoadjuvant therapy for HER2-positive BC but was not associated with patient outcomes or treatment benefit. However, the post-surgery prognostic value in patients that have not attained pCR warrants further investigation.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02568839. Registered on 6 October 2015.

The ever-expanding landscape of antibody-drug conjugates (ADCs) in solid tumors: A systematic review

BACKGROUND

The advent of targeted therapies signaled novel avenues for more optimal oncological outcomes. Antibody-drug conjugates (ADCs) have risen as a cornerstone of the ever-expanding targeted therapy era. The purpose of this systematic review is to delineate the rapidly evolving clinical landscape of ADCs for solid tumors.

METHODS

A literature search was performed in Medline, Embase and Cochrane databases for phase II and III clinical trials. Outcomes of interest were the objective response rate, overall survival, progression-free survival and adverse events.

RESULTS

A total of 92 clinical trials (76 phase II and 16 phase III) evaluated the efficacy and safety of ADCs for a plethora of solid tumors. Out of the 30 investigated ADCs, 8 have received approval by regulatory organizations for solid tumors. Currently, 52 phase III clinical trials for ADCs are ongoing.

CONCLUSION

ADCs have shown promising results for several solid tumors and various cancer settings.

Correlation of SUV on Early Interim PET with Recurrence-Free Survival and Overall Survival in Primary Operable HER2-Positive Breast Cancer (the TBCRC026 Trial)

Predictive biomarkers of response to human epidermal growth factor receptor 2 (HER2)-directed therapy are essential to inform treatment decisions. The TBCRC026 trial reported that early declines in tumor SUVs corrected for lean body mass (SULmax) on 18F-FDG PET/CT predicted a pathologic complete response (pCR) to HER2 therapy with neoadjuvant trastuzumab and pertuzumab (HP) without chemotherapy in estrogen receptor (ER)-negative, HER2-positive breast cancer. We hypothesized that 18F-FDG PET/CT SULmax parameters would predict recurrence-free survival (RFS) and overall survival (OS). Methods: Patients with stage II/III ER-negative, HER2-positive breast cancer received neoadjuvant HP (n = 88). pCR after HP alone was 22% (18/83), additional nonstudy neoadjuvant therapy was administered in 28% (25/88), and the majority received adjuvant therapy per physician discretion. 18F-FDG PET/CT was performed at baseline and at cycle 1, day 15 (C1D15). RFS and OS were summarized using the Kaplan-Meier method and compared between subgroups using logrank tests. Associations between 18F-FDG PET/CT (≥40% decline in SULmax between baseline and C1D15, or C1D15 SULmax ≤ 3) and pCR were evaluated using Cox regressions, where likelihood ratio CIs were reported because of the small numbers of events. Results: Median follow-up was 53.7 mo (83/88 evaluable), with 6 deaths and 14 RFS events. Estimated RFS and OS at 3 y was 84% (95% CI, 76%-92%) and 92% (95% CI, 87%-98%), respectively. A C1D15 SULmax of 3 or less was associated with improved RFS (hazard ratio [HR], 0.36; 95% CI, 0.11-1.05; P = 0.06) and OS (HR, 0.14; 95% CI, 0.01-0.85; P = 0.03), the latter statistically significant. The association of an SULmax decline of at least 40% (achieved in 59%) with RFS and OS did not reach statistical significance. pCR was associated with improved RFS (HR, 0.25; 95% CI, 0.01-1.24; P = 0.10) but did not reach statistical significance. Conclusion: For the first time, we report a potential association between a C1D15 SULmax of 3 or less on 18F-FDG PET/CT and RFS and OS outcomes in patients with ER-negative, HER2-positive breast cancer receiving neoadjuvant HP alone. If confirmed in future studies, this imaging-based biomarker may facilitate early individualization of therapy.

Antibody drug conjugates: hitting the mark in pancreatic cancer?

Pancreatic cancer is one of the most common causes of cancer-related death, and the 5-year survival rate has only improved marginally over the last decade. Late detection of the disease means that in most cases the disease has advanced locally and/or metastasized, and curative surgery is not possible. Chemotherapy is still the first-line treatment however, this has only had a modest impact in improving survival, with associated toxicities. Therefore, there is an urgent need for targeted approaches to better treat pancreatic cancer, while minimizing treatment-induced side-effects. Antibody drug conjugates (ADCs) are one treatment option that could fill this gap. Here, a monoclonal antibody is used to deliver extremely potent drugs directly to the tumor site to improve on-target killing while reducing off-target toxicity. In this paper, we review the current literature for ADC targets that have been examined in vivo for treating pancreatic cancer, summarize current and on-going clinical trials using ADCs to treat pancreatic cancer and discuss potential strategies to improve their therapeutic window.

Landscape of neoadjuvant therapy in HER2-positive breast cancer: a systematic review and network meta-analysis

BACKGROUND

The recommended preoperative approach for HER2-positive breast cancer is unclear. We aimed to investigate the following: i) what is the optimal neoadjuvant regimen and ii) whether anthracyclines could be excluded.

METHODS

A systematic literature search in Medline, Embase and Web of Science databases was performed. Studies had to satisfy the following criteria: i) randomised controlled trials (RCTs), ii) enroled patients treated preoperatively for HER2-positive BC (breast cancer), iii) at least one treatment group received an anti-HER2 agent, iv) available information of any efficacy end-point and v) published in English. A network meta-analysis with a frequentist framework using random-effects model was used to pool direct and indirect evidence. Pathologic complete response (pCR), event-free survival (EFS) and overall survival (OS) were the efficacy end-points of interest, and selected safety end-points were also analysed.

RESULTS

A total of 11,049 patients with HER2-positive BC (46 RCTs) were included in the network meta-analysis, and 32 different regimens were evaluated. Dual anti-HER2-therapy, with pertuzumab or tyrosine kinase inhibitors, combined with chemotherapy was significantly superior to trastuzumab and chemotherapy in terms of pCR, EFS and OS. However, a higher risk of cardiotoxicity was observed with dual anti-HER2-therapy. Anthracycline-based chemotherapy was not associated with better efficacy outcomes in comparison with non-anthracycline-based chemotherapy. In anthracycline-free regimens, the addition of carboplatin presented numerically better efficacy outcomes.

CONCLUSION

Dual HER2 blockade with chemotherapy is the recommended choice as neoadjuvant therapy for HER2-positive breast cancer, preferably by omitting anthracyclines in favour of carboplatin.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

Background

Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer.

Methods

From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively).

Findings

A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts.

Interpretation

Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer.

Funding

This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.