Local-Regional Therapy for Oligometastatic Colorectal Cancer.

ABSTRACT: Colorectal cancer is one of the most common malignancies in the United States as well as a leading cause of cancer-related death. Upward of 30% of patients ultimately develop metastatic disease, most commonly to the liver and lung. Untreated, patients have poor survival. Historically, patients with oligometastatic disease were treated with resection leading to long-term survival; however, there are many patients who are not surgical candidates. Innovations in thermal ablation, hepatic artery infusions, chemoembolization and radioembolization, and stereotactic ablative radiation have led to an expansion of patients eligible for local therapy. This review examines the evidence behind each modality for the most common locations of oligometastatic colorectal cancer.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Gas-Phase Acidities of Phosphorylated Amino Acids.

Gas-phase acidities and heats of formation have been predicted at the G3(MP2)/SCRF-COSMO level of theory for 10 phosphorylated amino acids and their corresponding amides, including phospho-serine (pSer), -threonine (pThr), and -tyrosine (pTyr), providing the first reliable set of these values. The gas-phase acidities (GAs) of the three named phosphorylated amino acids and their amides have been determined using proton transfer reactions in a Fourier transform ion cyclotron mass spectrometer. Excellent agreement was found between the experimental and predicted GAs. The phosphate group is the deprotonation site for pSer and pThr and deprotonation from the carboxylic acid generated the lowest energy anion for pTyr. The infrared spectra were calculated for six low energy anions of pSer, pThr, and pTyr. For deprotonated pSer and pThr, good agreement is found between the experimental IRMPD spectra and the calculated spectra for our lowest energy anion structure. For pTyr, the IR spectra for a higher energy phosphate deprotonated structure is in good agreement with experiment. Additional experiments tested electrospray ionization (ESI) conditions for pTyr and determined that variations in solvent, temperature, and voltage can result in a different experimental GA value, indicating that ESI conditions affect the conformation of the pTyr anion.

The biological and chemical basis for tissue-selective amyloid disease.

Factors controlling the onset and progression of extracellular amyloid diseases remain largely unknown. Central to disease etiology is the efficiency of the endoplasmic reticulum (ER) machinery that targets destabilized mutant proteins for degradation and the enhanced tendency of these variants to aggregate if secreted. We demonstrate that mammalian cells secrete numerous transthyretin (TTR) disease-associated variants with wild-type efficiency in spite of compromised folding energetics. Only the most highly destabilized TTR variants are subjected to ER-associated degradation (ERAD) and then only in certain tissues, providing insight into tissue selective amyloidosis. Rather than a "quality control" standard based on wild-type stability, we find that ER-assisted folding (ERAF), based on global protein energetics, determines the extent of export. We propose that ERAF (influenced by the energetics of the protein fold, chaperone enzyme distributions, and metabolite chaperones) in competition with ERAD defines the unique secretory aptitude of each tissue.

Native state stabilization by NSAIDs inhibits transthyretin amyloidogenesis from the most common familial disease variants.

Transthyretin (TTR) tetramer dissociation and misfolding affords a monomeric amyloidogenic intermediate that misassembles into aggregates including amyloid fibrils. Amyloidogenesis of wild-type (WT) TTR causes senile systemic amyloidosis (SSA), whereas fibril formation from one of the more than 80 TTR variants leads to familial amyloidosis, typically with earlier onset than SSA. Several nonsteroidal anti-inflammatory drugs (NSAIDs) stabilize the native tetramer, strongly inhibiting TTR amyloid fibril formation in vitro. Structure-based designed NSAID analogs are even more potent amyloid inhibitors. The effectiveness of several NSAIDs, including diclofenac, diflunisal, and flufenamic acid, as well as the diclofenac analog, 2-[(3,5-dichlorophenyl) amino] benzoic acid (inhibitor 1), has been demonstrated against WT TTR amyloidogenesis. Herein, the efficacy of these compounds at preventing acid-induced fibril formation and urea-induced tetramer dissociation of the most common disease-associated TTR variants (V30M, V122I, T60A, L58H, and I84S) was evaluated. Homotetramers of these variants were employed for the studies within, realizing that the tetramers in compound heterozygote patients are normally composed of a mixture of WT and variant subunits. The most common familial TTR variants were stabilized substantially by flufenamic acid and inhibitor 1, and to a lesser extent by diflunisal, against acid-mediated fibril formation and chaotrope denaturation, suggesting that this chemotherapeutic option is viable for patients with familial transthyretin amyloidosis.