Managing breast cancer in younger women: Challenges and solutions

Breast cancer in young women is relatively rare compared to breast cancer occurring in older women. Younger women diagnosed with breast cancer also tend to have a more aggressive biology and consequently a poorer prognosis than older women. In addition, they face unique challenges such as diminished fertility from premature ovarian failure, extended survivorship periods and its attendant problems, and the psychosocial impact of diagnosis, while still raising families. It is therefore imperative to recognize the unique issues that younger women face, and plan management in a multidisciplinary fashion to optimize clinical outcomes. This paper discusses the challenges of breast cancer management for young women, as well as specific issues to consider in diagnosis, treatment, and follow-up of such patients

Neoadjuvant Therapy in Operable Breast Cancer: Application to Triple Negative Breast Cancer

Systemic treatment for triple negative breast cancer (TNBC: negative for the expression of estrogen receptor and progesterone receptor and HER2 amplification) has been limited to chemotherapy options. Neoadjuvant chemotherapy induces tumor shrinkage and improves the surgical outcomes of patients with locally advanced disease and also identifies those at high risk of disease relapse despite today's standard of care. By using pathologic complete response as a surrogate endpoint, novel treatment strategies can be efficiently assessed. Tissue analysis in the neoadjuvant setting is also an important research tool for the identification of chemotherapy resistance mechanisms and new therapeutic targets. In this paper, we review data on completed and ongoing neoadjuvant clinical trials in patients with TNBC and discuss treatment controversies that face clinicians and researchers when neoadjuvant chemotherapy is employed

Proteogenomic markers of chemotherapy resistance and response in triple-negative breast cancer

UNLABELLED: Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2-M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31-33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. SIGNIFICANCE: Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31-33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

PURPOSE: We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. METHODS: TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV RESULTS: Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV CONCLUSIONS: We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

BACKGROUND: Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial. METHODS: Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images. FINDINGS: Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects. INTERPRETATION: Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies

Circulating tumour DNA characterisation of invasive lobular carcinoma in patients with metastatic breast cancer

BACKGROUND: Limited data exist to characterise molecular differences in circulating tumour DNA (ctDNA) for patients with invasive lobular carcinoma (ILC). We analysed metastatic breast cancer patients with ctDNA testing to assess genomic differences among patients with ILC, invasive ductal carcinoma (IDC), and mixed histology. METHODS: We retrospectively analysed 980 clinically annotated patients (121 ILC, 792 IDC, and 67 mixed histology) from three academic centers with ctDNA evaluation by Guardant360™. Single nucleotide variations (SNVs), copy number variations (CNVs), and oncogenic pathways were compared across histologies. FINDINGS: ILC was significantly associated with HR+ HER2 negative and HER2 low. SNVs were higher in patients with ILC compared to IDC or mixed histology (Mann Whitney U test, P \u3c 0.05). In multivariable analysis, HR+ HER2 negative ILC was significantly associated with mutations in CDH1 (odds ratio (OR) 9.4, [95% CI 3.3-27.2]), ERBB2 (OR 3.6, [95% confidence interval (CI) 1.6-8.2]), and PTEN (OR 2.5, [95% CI 1.05-5.8]) genes. CDH1 mutations were not present in the mixed histology cohort. Mutations in the PI3K pathway genes (OR 1.76 95% CI [1.18-2.64]) were more common in patients with ILC. In an independent cohort of nearly 7000 metastatic breast cancer patients, CDH1 was significantly co-mutated with targetable alterations (PIK3CA, ERBB2) and mutations associated with endocrine resistance (ARID1A, NF1, RB1, ESR1, FGFR2) (Benjamini-Hochberg Procedure, all q \u3c 0.05). INTERPRETATION: Evaluation of ctDNA revealed differences in pathogenic alterations and oncogenic pathways across breast cancer histologies with implications for histologic classification and precision medicine treatment. FUNDING: Lynn Sage Cancer Research Foundation, OncoSET Precision Medicine Program, and UL1TR001422

A randomized phase 2 study of neoadjuvant carboplatin and paclitaxel with or without atezolizumab in triple negative breast cancer (TNBC) - NCI 10013

Atezolizumab with chemotherapy has shown improved progression-free and overall survival in patients with metastatic PD-L1 positive triple negative breast cancer (TNBC). Atezolizumab with anthracycline- and taxane-based neoadjuvant chemotherapy has also shown increased pathological complete response (pCR) rates in early TNBC. This trial evaluated neoadjuvant carboplatin and paclitaxel with or without atezolizumab in patients with clinical stages II-III TNBC. The co-primary objectives were to evaluate if chemotherapy and atezolizumab increase pCR rate and tumor infiltrating lymphocyte (TIL) percentage compared to chemotherapy alone in the mITT population. Sixty-seven patients (ages 25-78 years; median, 52 years) were randomly assigned - 22 patients to Arm A, and 45 to Arm B. Median follow up was 6.6 months. In the modified intent to treat population (all patients evaluable for the primary endpoints who received at least one dose of combination therapy), the pCR rate was 18.8% (95% CI 4.0-45.6%) in Arm A, and 55.6% (95% CI 40.0-70.4%) in Arm B (estimated treatment difference: 36.8%, 95% CI 8.5-56.6%; p = 0.018). Grade 3 or higher treatment-related adverse events occurred in 62.5% of patients in Arm A, and 57.8% of patients in Arm B. One patient in Arm B died from recurrent disease during the follow-up period. TIL percentage increased slightly from baseline to cycle 1 in both Arm A (mean ± SD: 0.6% ± 21.0%) and Arm B (5.7% ± 15.8%) (p = 0.36). Patients with pCR had higher median TIL percentages (24.8%) than those with non-pCR (14.2%) (p = 0.02). Although subgroup analyses were limited by the small sample size, PD-L1-positive patients treated with chemotherapy and atezolizumab had a pCR rate of 75% (12/16). The addition of atezolizumab to neoadjuvant carboplatin and paclitaxel resulted in a statistically significant and clinically relevant increased pCR rate in patients with clinical stages II and III TNBC. (Funded by National Cancer Institute)

Impact of the COVID-19 pandemic on breast, colorectal, lung, and prostate cancer stage at diagnosis according to race

PURPOSE: To determine if the COVID-19 pandemic has further exacerbated racial disparities in late-stage presentation of breast, colorectal, lung, and prostate cancers. METHODS: We conducted a registry-based retrospective study of patients with newly reported diagnoses of breast, colorectal, lung, and prostate cancers between March 2019-June 2019 (pre-COVID-19) and March 2020-June 2020 (early-COVID-19). We compared the volume of new diagnoses and stage at presentation according to race between both periods. RESULTS: During the study period, a total of 3528 patients had newly diagnosed cancer; 3304 of which had known disease stages and were included in the formal analyses. 467 (14.1%) were Blacks, and 2743 were (83%) Whites. 1216 (36.8%) had breast, 415 (12.6%) had colorectal, 827 (25%) had lung, and 846 (25.6%) had prostate cancers, respectively. The pre-COVID-19 period included 2120 (64.2%), and the early-COVID-19 period included 1184 (35.8%), representing a proportional 44.2% decline in the volume of new cases of breast, colorectal, lung, and prostate cancers, p \u3c 0.0001. Pre-COVID-19, 16.8% were diagnosed with metastatic disease, versus 20.4% early-COVID-19, representing a proportional increase of 21.4% in the numbers of new cases with metastatic disease, p = 0.01. There was a non-significant proportional decline of 1.9% in Black patients diagnosed with non-metastatic breast, colorectal, lung, and prostate cancers early-COVID-19 (p = 0.71) and a non-significant proportional increase of 7% in Black patients diagnosed with metastatic disease (p = 0.71). Difference-in-difference analyses showed no statistically significant differences in metastatic presentation comparing Black to White patients. CONCLUSION: While we identified substantial reductions in the volume of new cancer diagnoses and increases in metastatic presentations due to the COVID-19 pandemic, the impact was similar for White and Black patients

Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors with chemotherapy

Although immunotherapy can offer profound clinical benefit for patients with a variety of difficult-to-treat cancers, many tumors either do not respond to upfront treatment with immune checkpoint inhibitors (ICIs) or progressive/recurrent disease occurs after an interval of initial control. Improved response rates have been demonstrated with the addition of ICIs to cytotoxic therapies, leading to approvals from the US Food and Drug Administration and regulatory agencies in other countries for ICI-chemotherapy combinations in a number of solid tumor indications, including breast, head and neck, gastric, and lung cancer. Designing trials for patients with tumors that do not respond or stop responding to treatment with immunotherapy combinations, however, is challenging without uniform definitions of resistance. Previously, the Society for Immunotherapy of Cancer (SITC) published consensus definitions for resistance to single-agent anti-programmed cell death protein 1 (PD-1). To provide guidance for clinical trial design and to support analyses of emerging molecular and cellular data surrounding mechanisms of resistance to ICI-based combinations, SITC convened a follow-up workshop in 2021 to develop consensus definitions for resistance to multiagent ICI combinations. This manuscript reports the consensus clinical definitions for combinations of ICIs and chemotherapies. Definitions for resistance to ICIs in combination with targeted therapies and with other ICIs will be published in companion volumes to this paper