A system for generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject, the system including one or more electronic processing devices that: a) obtain subject data indicative of a sequence of a nucleic acid molecule from the subject; b) analyze the subject data to identify single nucleotide variations (SNVs) within the nucleic acid molecule; c) determine a plurality of metrics using the identified SNVs, the plurality of metrics including metrics from a deaminase motif metric group associated with SNVs in one or more deaminase motifs and a codon context metric group wherein the metrics of this group are associated with a codon context of SNVs, and optionally from one or more of other metric groups including: i) a transition/transversion metric group including metrics associated with SNVs that are transitions or transversions; ii) a synonymous/non-synonymous metric group including metrics associated with SNVs that are synonymous or non-synonymous; iii) a strand bias metric group wherein the metrics of this group are associated with strand bias of SNVs; iv) a strand specific metric group that includes metrics associated with SNVs on a specific strand; and v) an AT/GC metric group wherein the metrics of this group are associated the number, percentage or ratio of SNVs that target adenine and thymine, and/or guanine and cytidine; d) apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy, the at least one computational model embodying a relationship between a responsiveness to cancer therapy and the plurality of metrics and being derived by applying machine learning to a plurality of reference metrics obtained from reference subjects having a known responsiveness to cancer therapy. a) obtain subject data indicative of a sequence of a nucleic acid molecule from the subject; b) analyze the subject data to identify single nucleotide variations (SNVs) within the nucleic acid molecule; c) determine a plurality of metrics using the identified SNVs, the plurality of metrics including metrics from a deaminase motif metric group associated with SNVs in one or more deaminase motifs and a codon context metric group wherein the metrics of this group are associated with a codon context of SNVs, and optionally from one or more of other metric groups including: i) a transition/transversion metric group including metrics associated with SNVs that are transitions or transversions; ii) a synonymous/non-synonymous metric group including metrics associated with SNVs that are synonymous or non-synonymous; iii) a strand bias metric group wherein the metrics of this group are associated with strand bias of SNVs; iv) a strand specific metric group that includes metrics associated with SNVs on a specific strand; and v) an AT/GC metric group wherein the metrics of this group are associated the number, percentage or ratio of SNVs that target adenine and thymine, and/or guanine and cytidine; i) a transition/transversion metric group including metrics associated with SNVs that are transitions or transversions; ii) a synonymous/non-synonymous metric group including metrics associated with SNVs that are synonymous or non-synonymous; iii) a strand bias metric group wherein the metrics of this group are associated with strand bias of SNVs; iv) a strand specific metric group that includes metrics associated with SNVs on a specific strand; and v) an AT/GC metric group wherein the metrics of this group are associated the number, percentage or ratio of SNVs that target adenine and thymine, and/or guanine and cytidine; d) apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy, the at least one computational model embodying a relationship between a responsiveness to cancer therapy and the plurality of metrics and being derived by applying machine learning to a plurality of reference metrics obtained from reference subjects having a known responsiveness to cancer therapy.