diff --git a/pvactools/lib/pipeline.py b/pvactools/lib/pipeline.py
index 4f817624e..a74978e06 100644
--- a/pvactools/lib/pipeline.py
+++ b/pvactools/lib/pipeline.py
@@ -367,7 +367,6 @@ def parse_outputs(self, chunks):
             for a in self.alleles:
                 for epl in self.epitope_lengths:
                     split_iedb_output_files = []
-                    status_message("Parsing binding predictions for Allele %s and Epitope Length %s - Entries %s" % (a, epl, fasta_chunk))
                     for method in self.prediction_algorithms:
                         prediction_class = globals()[method]
                         prediction = prediction_class()
@@ -673,7 +672,6 @@ def parse_outputs(self, chunks, length):
                 fasta_chunk = "%d-%d" % (split_start*2-1, split_end*2)
             for a in self.alleles:
                 split_iedb_output_files = []
-                status_message("Parsing binding predictions for Allele %s and Epitope Length %s - Entries %s" % (a, length, fasta_chunk))
                 for method in self.prediction_algorithms:
                     prediction_class = globals()[method]
                     prediction = prediction_class()
diff --git a/pvactools/tools/pvacfuse/run.py b/pvactools/tools/pvacfuse/run.py
index 95978ac14..4ce7c323d 100644
--- a/pvactools/tools/pvacfuse/run.py
+++ b/pvactools/tools/pvacfuse/run.py
@@ -220,9 +220,10 @@ def main(args_input = sys.argv[1:]):
                 pipeline = PvacbindPipeline(**run_arguments)
                 pipeline.execute()
                 intermediate_output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
-                output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.final.tsv".format(args.sample_name))
-                append_columns(intermediate_output_file, "{}.tsv".format(input_file), output_file)
-                output_files.append(output_file)
+                if os.path.exists(intermediate_output_file):
+                    output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.final.tsv".format(args.sample_name))
+                    append_columns(intermediate_output_file, "{}.tsv".format(input_file), output_file)
+                    output_files.append(output_file)
                 if epitope_length == max(epitope_lengths):
                     # copy fasta to output dir
                     fasta_file = os.path.join(output_dir, "{}.fasta".format(args.sample_name))
@@ -239,10 +240,12 @@ def main(args_input = sys.argv[1:]):
                 #!!! make below call to create_net_class_report
                 #create_combined_reports(output_files, all_epitopes_file, filtered_file, True, args)
                 create_net_class_report(output_files, all_epitopes_file, filtered_file, args, run_arguments)
+            else:
+                print("\nNo processable fusions found. Aborting.\n")
         elif len(prediction_algorithms) == 0:
             print("No MHC class {} prediction algorithms chosen. Skipping MHC class I predictions.".format(mhc_class))
         elif len(alleles) == 0:
-            print("No MHC class{} alleles chosen. Skipping MHC class I predictions.".format(mhc_class))
+            print("No MHC class {} alleles chosen. Skipping MHC class II predictions.".format(mhc_class))
 
     if len(class_i_prediction_algorithms) > 0 and len(class_i_alleles) > 0 and len(class_ii_prediction_algorithms) > 0 and len(class_ii_alleles) > 0:
         print("Creating combined reports")