Background and Purpose: No longer considered a single disease entity, breast cancer is being classified into several distinct molecular subtypes based on gene expression profiling. These subtypes appear to carry prognostic implications and have the potential to be incorporated into treatment decisions. In this study, we evaluated patterns of local recurrence (LR), distant metastasis (DM), and association of survival with molecular subtype in breast cancer patients in the post–adjuvant radiotherapy setting.Material and Methods: The medical records of 1,088 consecutive, non-metastatic breast cancer patients treated at a single institution between 2004 and 2012 were reviewed. Estrogen/progesterone receptors (ER/PR) and human epidermal growth factor receptor-2 (HER2) enrichment were evaluated by immunohistochemistry. Patients were categorized into one of four subtypes: luminal-A (LA; ER/PR+, HER2-, Grade 1-2), luminal-B (LB; ER/PR+, HER2-, Grade > 2), HER2 over-expression (HER2; ER/PR-, HER2+), and triple negative (TN; ER/PR-, HER2-). Results: The median follow-up time was 6.9 years. During the follow-up, 16% (174/1,088) of patients failed initial treatment and developed either LR (48) or DM (126). The prevalence of LR was the highest in TN (12%) and the lowest in LA (2%). Breast or chest wall relapse was the most frequent site (≈80%) of recurrence in LA, LB, and HER2 subtypes, whereas the regional lymph nodes and chest wall were the common sites of relapse in the TN group (50.0%). DM rates were 6.4% in LA, 12.1% in LB, 19.2% in HER2, and 27.4% in TN subgroups. Five-year survival rates were 84%, 83%, 84%, and 77% in the LA, LB, HER2 and TN subgroups, respectively. There was a statistically significant association between survival and molecular subtypes in an univariate analysis. In the adjusted multivariate analysis, the following variables were independent prognostic factors for survival: T stage, N stage, and molecular subtype.Conclusions: Of the four subtypes, the LA subtype tends to have the best prognosis, fairly high survival, and low recurrent or metastases rates. The TN and HER2 subtypes of breast cancer were associated with significantly poorer overall survival and prone to earlier recurrence and metastases. Our results demonstrate a significant association between molecular subtype and survival. The risk of death and relapse/metastases increases fewfold in TN compared to LA. Future prospective studies are warranted and could ultimately lead to the tailoring of adjuvant radiotherapy treatment fields based on both molecular subtype and the more conventional clinicopathologic characteristics.

Therapeutic vaccination of B cell lymphoma patients with tumor-specific Ig (idiotype, or Id) chemically coupled to the immunogenic foreign carrier protein keyhole limpet hemocyanin (KLH) using glutaraldehyde has shown promising results in early clinical trials, and phase III trials are underway. However, glutaraldehyde Id-KLH vaccines fail to elicit anti-Id immune and clinical responses in many patients, possibly because glutaraldehyde reacts with lysine, cysteine, tyrosine, and histidine residues, damaging critical immunogenic epitopes. A sulfhydryl-based tumor Ag-carrier protein conjugation system using maleimide chemistry was used to enhance the efficacy of Id-KLH vaccines. Maleimide Id-KLH conjugates eradicated A20 lymphoma from most tumor-bearing mice, whereas glutaraldehyde Id-KLH had little efficacy. Maleimide Id-KLH elicited tumor-specific IgG Abs and T cells, with CD8+ T cells being the major effectors of antilymphoma immunity. Maleimide Id-KLH vaccines also demonstrated superior efficacy in 38C13 and BCL-1 lymphoma models, where Abs were shown to be critical for protection. Importantly, standard glutaraldehyde Id-KLH conjugation procedures could result in “overconjugation” of the tumor Ag, leading to decreased efficacy, whereas the heterobifunctional maleimide-based conjugation yielded potent vaccine product regardless of conjugation duration. Under lysosomal processing conditions, the Id-carrier protein linkage was cleavable only after maleimide conjugation. Maleimide KLH conjugation was easily performed with human Igs analogous to those used in Id-KLH clinical trials. These data support the evaluation of sulfhydryl-based Id-KLH vaccines in lymphoma clinical trials and possibly the use of tumor Ag-carrier protein vaccines for other cancers.

The anti-CD20 monoclonal antibody rituximab (Rituxan) has become a mainstay in the treatment of B cell non-Hodgkin lymphomas. The mechanisms of action for rituximab include antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity, and apoptosis induction. Combination of anti-CD20 antibodies with immunostimulatory agents may improve their efficacy via enhancement of one or more of these mechanisms. Toll-like receptor 9 agonist CpG oligodeoxynucleotides administered systemically have been studied in clinical trials with and without rituximab. However, recent data suggest that intratumoral (IT) delivery of CpG has advantages in the treatment of tumors. Using a syngeneic murine B cell lymphoma line expressing human CD20, we found that IT, but not systemically administered CpG significantly improved the efficacy of rituximab against 7-day established tumors. Rituximab plus IT CpG could eradicate tumors from 42% of mice, whereas systemically administered CpG, with or without rituximab, did not achieve tumor eradication. Both natural killer cells and complement participated in the cure of tumors by rituximab plus IT CpG, apparently by increasing tumor cell sensitivity to complement and ADCC lysis, and by augmenting the cytotoxicity of ADCC effectors. No role for T cells in mediating tumor eradication was demonstrated in this model. These results suggest that previous clinical trials in B cell lymphoma combining systemic administration of CpG with rituximab may have employed suboptimal routes of CpG delivery. Future trials combining IT CpG with anti-CD20 antibodies or the antibody-mediated targeting of CpG directly to the sites of B cell lymphoma may thus be warranted.

We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes.

Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.